Fundamentals of Volunteered Geographic Information in Disaster Management Related to Floods

*Stathis G. Arapostathis*

## **Abstract**

The main purpose of this chapter is to introduce fundamental knowledge regarding the notion of volunteered geographic information (VGI) and its applications in disaster management (DM) of events related to floods. Initially, the meaning of the term is defined along with certain properties and general trends that characterize VGI. A brief literature review unfolds the range of activities that compose that certain term, along with its applications to flood event management. Those applications cover significant aspects of both VGI and DM cycle: from participatory activities of volunteers up to pure data analysis, extracted from social media and other VGI sources, while, in terms of DM cycle, from mitigation up to response and recovery. Finally, a set of four main clusters of open challenges is addressed. Those clusters accumulate the vast majority of open topics on this research field.

**Keywords:** volunteered geographic information, VGI, disaster management, floods, flood event management, social networks

## **1. Introduction**

Flood events occur with high frequency globally, due to reasons related to climate change, to deforestation, and to problematic urban design of many high-populated areas. As a result, the effective disaster management (DM) of flood events, aspiring to mitigate the occurrence along with the negative consequences of those incidents, has emerged.

The current chapter provides a comprehensive interview of an interdisciplinary research regarding the use of volunteered geographic information (VGI) in procedures, methods, and strategies related to DM of flood events.

The next sections introduce the notion of VGI and its applications to DM of events related to floods. Various similar terms are mentioned along with a literature review which unfolds the range of activities that compose the so-called applications of VGI to flood event management. Those applications cover significant aspects of both VGI and DM components. In specific, the scope of the applications ranges from participatory activities of volunteers up to pure VGI data analysis, generated from social media content and other VGI sources. In terms of DM, those

applications contribute significantly to various phases of the DM cycle: from prevention and preparedness up to mitigation, response, and recovery. Furthermore, a set of four main clusters of open challenges of the research field is addressed and described. The chapter ends with a conclusions section which accumulates the essential assumptions of this research topic.

#### **1.1 VGI introduction: definition, scope data sources, properties and characteristics, and applications**

The term volunteered geographic information was initially defined by Michael Goodchild (2007) who used it to describe the act of having citizens, without having any related scientific background, produce geographic information. In contrary to the conventional flow in research which is from the scientific world to the society (top to bottom), the "phenomenon" of VGI followed a reverse path [1]. The enormous rhythm of voluntarily generated data forced the scientific community to identify this modern trend initially and to research ways for effective exploitation, sequentially, in benefit of a wide range of scientific fields.

Many similar terms have been used in the international scientific literature, including collaboratively contributed geographic information (CCGI, [2, 3]), citizen observatory [4, 5], neo-geography [6], ubiquitous cartography [7], participatory geographic information systems [8], user-generated spatial content [9], crowdsourced geographic information [10], citizen science [11–13], citizen sensing [14, 15], and human sensor network [16, 17]. All the above terms overlap in their definition either partially (i.e., citizen science) or totally (i.e., CCGI), depending on the spatial dimension of the generated information.

Initially the VGI term described digital data production activities, as a result of the Web 2.0 technologies which evolved user interaction through the World Wide Web [18, 19]. However, as the volunteered procedures with spatial context evolved through the last decade, and considering the similar terms mentioned previously, the scope of VGI, is highly related, among others, to digital activities for community self-organizing [20] or other participatory activities that may not contain computer interaction at all [21, 22].

The high rhythm of VGI data production in some cases is so enormous that it initially led to assumptions regarding a geography without geographers and to wikification of GIS, describing thus the transformation of GIS to participatory due to VGI data, in a way similar to the articles of the well-known Wikipedia [23]. After a few years of research though, VGI concluded to emerge as a valuable tool for research instead of a replacer of geography [24].

#### **1.2 VGI data sources**

The VGI data sources can be grouped into two main categories: (1) the conventional, pure, structured, or purpose-driven VGI sources and (2) the unstructured, unintentionally driven ones.

The first category consists of specialized web spots in which the users are invited to report or generate specific information, by following some basic rules or some simple procedures. Probably the most popular representative of this category is OpenStreetMap (OSM), developed by Steve Coast. OSM counts millions of users who contribute to mapping information, while the mapping quality in high-populated cities of the world is equivalent to one of the conventional mapping data providers [25, 26]. Regarding floods, there is published research for manipulating OSM content for the needs of flood event management [27].

**159**

*Fundamentals of Volunteered Geographic Information in Disaster Management Related to Floods*

Various other specialized VGI sources, which focus on DM, are based on the Ushahidi platform. Ushahidi means testimony in the language of Suachili. It was initially developed for mapping violent incidents in Kenya during the countries post-electoral events in 2008. Since then, Ushahidi has been evolved in an organization which provides web software for crisis situations. The platform has been widely used for DM purposes, of natural events [28, 29], while applications exclusively

The second category of VGI data sources consists of popular web spots through which the users generate geo-information unintentionally. Those VGI sources include almost all of the popular social networks (Facebook, Twitter, WeChat, YouTube). Considering the billions of users of the social media, the volume of produced information is tremendous. While numerous researches are based on the exploitation of those data. Moreover, as the use of that category of sources, in developing countries, is constantly rising [30], a large volume of information regarding floods is available, contributing thus to data availability which is characterized as problematic [31]. Apart from the latter, the enormous volume of generated information can contribute significantly in the emergency response of a flood-disastrous event, as the immediate

A significant property of VGI is related to conventional VGI sources and its compliance to specifications [23]. It is generally accepted that the volunteers tend to ignore strict specification rules as a really disciplined data production could kill their interest in generating data [32, 33]. Well-designed user interfaces and purpose-driven approaches for generating data are considered efficient ways in order to

Moreover, some of the most important aspects of VGI that need to be assessed are quality and credibility. Regarding both, Linus' law seems to be applied in the vast majority of cases [25, 26, 33, 34]. Linus' law is linked to the Linux operating system and implies that the more programmers develop a software, the less bugs the software will have [35]. In terms of VGI, Linus' law implies that the more volunteers appear in a

Even though Linus' law seems to be applied in most of the cases, latest research, to unstructured VGI sources, like social networks, demonstrated that the information produced by the majority of the users might be wrong. Until today, those cases usually refer to information regarding controversial and subjective topics that have political orientations and impact. An indicative example is the spread of fake news during the presidential elections of 2016, through Twitter [36, 37]. Moreover, there are a lot of cases in which many researchers propose various quality frameworks for assessing VGI different than the validity of Linus' law [38]. In terms of DM of physical events like floods though, the validity of Linus' law seems to be effective. Finally, another significant property of VGI refers to the spatial heterogeneity of the produced spatial content. Even if in a certain area the quality of the produced information may be considered as sufficient, in other areas, data quality may be proven significantly different. An indicative example is presented in [39] in which a comparison was performed, between the spatial distribution of flood events extracted from VGI and the floods that were reported in official authoritative sources. While in various parts of the world the information was equivalent to the official data, in other areas there was missing information. As a result, assessments of VGI data in areas of interest always need to be performed in order to be assured

certain region, the more accurate and complete the information will become.

that the data quality is sufficient for the use that it is designated for.

regarding flood events are analyzed in the following sections.

information is vital for an effective rapid response.

increase the amount of generated formed information.

**1.3 Characteristics and properties of VGI**

*DOI: http://dx.doi.org/10.5772/intechopen.92225*

#### *Fundamentals of Volunteered Geographic Information in Disaster Management Related to Floods DOI: http://dx.doi.org/10.5772/intechopen.92225*

Various other specialized VGI sources, which focus on DM, are based on the Ushahidi platform. Ushahidi means testimony in the language of Suachili. It was initially developed for mapping violent incidents in Kenya during the countries post-electoral events in 2008. Since then, Ushahidi has been evolved in an organization which provides web software for crisis situations. The platform has been widely used for DM purposes, of natural events [28, 29], while applications exclusively regarding flood events are analyzed in the following sections.

The second category of VGI data sources consists of popular web spots through which the users generate geo-information unintentionally. Those VGI sources include almost all of the popular social networks (Facebook, Twitter, WeChat, YouTube). Considering the billions of users of the social media, the volume of produced information is tremendous. While numerous researches are based on the exploitation of those data. Moreover, as the use of that category of sources, in developing countries, is constantly rising [30], a large volume of information regarding floods is available, contributing thus to data availability which is characterized as problematic [31]. Apart from the latter, the enormous volume of generated information can contribute significantly in the emergency response of a flood-disastrous event, as the immediate information is vital for an effective rapid response.

## **1.3 Characteristics and properties of VGI**

A significant property of VGI is related to conventional VGI sources and its compliance to specifications [23]. It is generally accepted that the volunteers tend to ignore strict specification rules as a really disciplined data production could kill their interest in generating data [32, 33]. Well-designed user interfaces and purpose-driven approaches for generating data are considered efficient ways in order to increase the amount of generated formed information.

Moreover, some of the most important aspects of VGI that need to be assessed are quality and credibility. Regarding both, Linus' law seems to be applied in the vast majority of cases [25, 26, 33, 34]. Linus' law is linked to the Linux operating system and implies that the more programmers develop a software, the less bugs the software will have [35]. In terms of VGI, Linus' law implies that the more volunteers appear in a certain region, the more accurate and complete the information will become.

Even though Linus' law seems to be applied in most of the cases, latest research, to unstructured VGI sources, like social networks, demonstrated that the information produced by the majority of the users might be wrong. Until today, those cases usually refer to information regarding controversial and subjective topics that have political orientations and impact. An indicative example is the spread of fake news during the presidential elections of 2016, through Twitter [36, 37]. Moreover, there are a lot of cases in which many researchers propose various quality frameworks for assessing VGI different than the validity of Linus' law [38]. In terms of DM of physical events like floods though, the validity of Linus' law seems to be effective.

Finally, another significant property of VGI refers to the spatial heterogeneity of the produced spatial content. Even if in a certain area the quality of the produced information may be considered as sufficient, in other areas, data quality may be proven significantly different. An indicative example is presented in [39] in which a comparison was performed, between the spatial distribution of flood events extracted from VGI and the floods that were reported in official authoritative sources. While in various parts of the world the information was equivalent to the official data, in other areas there was missing information. As a result, assessments of VGI data in areas of interest always need to be performed in order to be assured that the data quality is sufficient for the use that it is designated for.

*Flood Impact Mitigation and Resilience Enhancement*

essential assumptions of this research topic.

**characteristics, and applications**

dimension of the generated information.

research instead of a replacer of geography [24].

for the needs of flood event management [27].

interaction at all [21, 22].

**1.2 VGI data sources**

unintentionally driven ones.

applications contribute significantly to various phases of the DM cycle: from prevention and preparedness up to mitigation, response, and recovery. Furthermore, a set of four main clusters of open challenges of the research field is addressed and described. The chapter ends with a conclusions section which accumulates the

The term volunteered geographic information was initially defined by Michael Goodchild (2007) who used it to describe the act of having citizens, without having any related scientific background, produce geographic information. In contrary to the conventional flow in research which is from the scientific world to the society (top to bottom), the "phenomenon" of VGI followed a reverse path [1]. The enormous rhythm of voluntarily generated data forced the scientific community to identify this modern trend initially and to research ways for effective exploitation,

Many similar terms have been used in the international scientific literature, including collaboratively contributed geographic information (CCGI, [2, 3]), citizen observatory [4, 5], neo-geography [6], ubiquitous cartography [7], participatory geographic information systems [8], user-generated spatial content [9], crowdsourced geographic information [10], citizen science [11–13], citizen sensing [14, 15], and human sensor network [16, 17]. All the above terms overlap in their definition either partially (i.e., citizen science) or totally (i.e., CCGI), depending on the spatial

Initially the VGI term described digital data production activities, as a result of the Web 2.0 technologies which evolved user interaction through the World Wide Web [18, 19]. However, as the volunteered procedures with spatial context evolved through the last decade, and considering the similar terms mentioned previously, the scope of VGI, is highly related, among others, to digital activities for community self-organizing [20] or other participatory activities that may not contain computer

The high rhythm of VGI data production in some cases is so enormous that it initially led to assumptions regarding a geography without geographers and to wikification of GIS, describing thus the transformation of GIS to participatory due to VGI data, in a way similar to the articles of the well-known Wikipedia [23]. After a few years of research though, VGI concluded to emerge as a valuable tool for

The VGI data sources can be grouped into two main categories: (1) the conventional, pure, structured, or purpose-driven VGI sources and (2) the unstructured,

The first category consists of specialized web spots in which the users are invited to report or generate specific information, by following some basic rules or some simple procedures. Probably the most popular representative of this category is OpenStreetMap (OSM), developed by Steve Coast. OSM counts millions of users who contribute to mapping information, while the mapping quality in high-populated cities of the world is equivalent to one of the conventional mapping data providers [25, 26]. Regarding floods, there is published research for manipulating OSM content

**1.1 VGI introduction: definition, scope data sources, properties and** 

sequentially, in benefit of a wide range of scientific fields.

**158**

## **2. DM and VGI**

DM is the term that describes the scientific and operational activities and strategies which focus on mitigating the negative consequences of a catastrophic event occurrence. In general DM consisted of five main parts that compose the DM cycle. Those parts are (A) prevention, (B) mitigation, (C) preparedness, (D) response relief, and (E) recovery, divided in rehabilitation and reconstruction [40]. For each part there is a plethora of published research, while the range of events that are confronted through DM is pretty large: from political crisis situations and wars up to physical events such as floods, earthquakes, and fire events [41].

The general notion of VGI has been emerged as an important component that aspires to contribute to each one of the components of the DM cycle [42, 43]. Besides, the importance of volunteered activities in the DM procedures is clearly stated in the Sendai Framework for Disaster Risk Reduction of the United Nations [44], according to which the role of volunteers and community-based entities in general is to collaborate with authorities by providing "specific knowledge, and pragmatic guidance."

Meaningful ways of contribution according to each type of disastrous event though are still a challenge [45, 46]. Specifically regarding flood event management, in the following sections, various indicative applications of VGI for each one of the DM cycle components are analyzed.

## **3. Applications of VGI in DM of flood events**

Numerous published researches focus on utilizing VGI data sources for DM of flood events.

In terms of flood identification, in [39] a Twitter corpus consisting of 87.6 million tweets was analyzed, leading to the identification of 10.000 flood events, globally. The main steps of methodology applied and included initially geo-referencing of the tweets and, sequentially, identifying flood events in the geo-parsed content.

In terms of tracking a flood event, in [47] the contribution of unconventional VGI data sources (social networks) was assessed, for DM purposes. The research focuses on the devastating Queensland floods, which occurred in Australia from December 2010 up to February 2011. Those floods caused damages to more than 30 cities and rural communities in southern and western Queensland, while various agricultural sub-areas were inundated. The cost of the floods was about 5 billion Australian dollars. From a VGI point of view, the social networks Facebook and Twitter were used as data sources for extracting related information. Apart from the text of each post, embedded photos and videos were processed, identifying thus various sub-events. During the unfoldness of the floods, about 15 k tweets were posted per hour. Among the conclusions it is stated that VGI contributed significantly to the tracking and provided immediate and in-depth information, crucial for prevention, mitigation, preparedness, and response tasks of the DM cycle. In addition, they stated that by using VGI, the enhancement of their emergency situation awareness can lead to better decisions in planning operations for giving aid, not concluded.

The above assumption was verified in similar research [48], regarding the Colorado floods, occurring in the United States in 2013. The significance of correctly tracking all the phases of a natural disastrous event emerged, completely documenting that the negative impact of similar flood events that may potentially occur in the future can be minimized. Moreover, VGI data sources were able to fill an important gap of information regarding the floods, especially since the flood

**161**

*Fundamentals of Volunteered Geographic Information in Disaster Management Related to Floods*

occurrence, until the time that the scientific teams arrived in the area. In terms of methodology, the basic components include collection of tweets published within 9 days since the flood occurrence and the classification of those, to specific categories, including (1) geo-tagged tweets, (2) tweets containing obvious URLs to photos and videos, (3) tweets containing place names, and (4) tweets containing structural

Apart from tracking, the significance of rapidly produced information to authorities and DM stakeholders is emphasized on the international research [42, 49–51] as timely information is vital for the emergency response phase of the DM procedures. Moreover, the lack of information increases radically the budget that needs to be allocated for restoration. VGI sources have the potential to significantly contribute to

In [53] a method for extracting flood event-related information through VGI sources was presented. Their extensive research provides meaningful insights regarding the most effective automated classification methods for dividing the posted information into certain categories. From a DM perspective, they focused on event detection of pluvial and fluvial flood events, while the collection of specialized information that could be extracted through geo-tagged photos contributed effectively to tracking and to verifying conventional hydrological models. Moreover, in [46, 54] methodologies for effective processing of social network data for DM purposes of flood events are presented. Among the main findings is that effective classification and geo-referencing can lead to advanced insights regarding DM of flood events. Moreover by automating the methods, mapping of consequences of a flood event can be performed in real time, contributing signifi-

As stated in previous sections, the general notion of VGI is not strictly related to digital data procedures but also highly related to participatory approaches. After all, community involvement has been emerged as an important part of the DM operational activities, as by imbuing the community with a sense of ownership of the risk reduction process, resilience to deal with natural hazards is increased [19]. Moreover those approaches can be proven vital, especially in developing countries, which are expected to confront with the major consequences of the climate change, despite their minimum contribution to the problem [55], while data availability in many cases is affected, due to laws, security protocols, illiteracy, cultural barriers, and economic reasons [31]. In addition, the budget needed for organizing can be minimized by engaging local authorities to provide premises and by using open-source software

solutions [56] for collecting and processing information related to floods.

An interesting approach was presented in [22] who refer to the Chametla community located in Baja California that aimed to reduce the risk of negative consequences in the event of a potential flood occurrence in Baja California, Mexico. The community received appropriate training by experts. In specific, they organized a workshop, in Chametla, in which the participants were able to annotate on printed satellite imagery their property along with various spots of the area that are considered vulnerable to floods, building thus a related map. Sequentially, they presented their results, and upon related discussions, they were able to correct and adjust various spots on the map. The output was reviewed by risk management experts who provided additional corrections. The final map was created by a GIS technician who digitally mapped all the printed information. The workshop participants created an ordered list of tasks that they could do in order to minimize the area's vulnerability to the floods. Those tasks included, among others, the pavement

*DOI: http://dx.doi.org/10.5772/intechopen.92225*

terms, determined by the engineering team.

cantly to risk response of a flood event.

**3.1 Participatory approaches**

that part [52].

#### *Fundamentals of Volunteered Geographic Information in Disaster Management Related to Floods DOI: http://dx.doi.org/10.5772/intechopen.92225*

occurrence, until the time that the scientific teams arrived in the area. In terms of methodology, the basic components include collection of tweets published within 9 days since the flood occurrence and the classification of those, to specific categories, including (1) geo-tagged tweets, (2) tweets containing obvious URLs to photos and videos, (3) tweets containing place names, and (4) tweets containing structural terms, determined by the engineering team.

Apart from tracking, the significance of rapidly produced information to authorities and DM stakeholders is emphasized on the international research [42, 49–51] as timely information is vital for the emergency response phase of the DM procedures. Moreover, the lack of information increases radically the budget that needs to be allocated for restoration. VGI sources have the potential to significantly contribute to that part [52].

In [53] a method for extracting flood event-related information through VGI sources was presented. Their extensive research provides meaningful insights regarding the most effective automated classification methods for dividing the posted information into certain categories. From a DM perspective, they focused on event detection of pluvial and fluvial flood events, while the collection of specialized information that could be extracted through geo-tagged photos contributed effectively to tracking and to verifying conventional hydrological models.

Moreover, in [46, 54] methodologies for effective processing of social network data for DM purposes of flood events are presented. Among the main findings is that effective classification and geo-referencing can lead to advanced insights regarding DM of flood events. Moreover by automating the methods, mapping of consequences of a flood event can be performed in real time, contributing significantly to risk response of a flood event.

#### **3.1 Participatory approaches**

*Flood Impact Mitigation and Resilience Enhancement*

as floods, earthquakes, and fire events [41].

of the DM cycle components are analyzed.

**3. Applications of VGI in DM of flood events**

DM is the term that describes the scientific and operational activities and strategies which focus on mitigating the negative consequences of a catastrophic event occurrence. In general DM consisted of five main parts that compose the DM cycle. Those parts are (A) prevention, (B) mitigation, (C) preparedness, (D) response relief, and (E) recovery, divided in rehabilitation and reconstruction [40]. For each part there is a plethora of published research, while the range of events that are confronted through DM is pretty large: from political crisis situations and wars up to physical events such

The general notion of VGI has been emerged as an important component that aspires to contribute to each one of the components of the DM cycle [42, 43]. Besides, the importance of volunteered activities in the DM procedures is clearly stated in the Sendai Framework for Disaster Risk Reduction of the United Nations [44], according to which the role of volunteers and community-based entities in general is to collaborate with authorities by providing "specific knowledge, and

Meaningful ways of contribution according to each type of disastrous event though are still a challenge [45, 46]. Specifically regarding flood event management, in the following sections, various indicative applications of VGI for each one

Numerous published researches focus on utilizing VGI data sources for DM of

In terms of tracking a flood event, in [47] the contribution of unconventional VGI data sources (social networks) was assessed, for DM purposes. The research focuses on the devastating Queensland floods, which occurred in Australia from December 2010 up to February 2011. Those floods caused damages to more than 30 cities and rural communities in southern and western Queensland, while various agricultural sub-areas were inundated. The cost of the floods was about 5 billion Australian dollars. From a VGI point of view, the social networks Facebook and Twitter were used as data sources for extracting related information. Apart from the text of each post, embedded photos and videos were processed, identifying thus various sub-events. During the unfoldness of the floods, about 15 k tweets were posted per hour. Among the conclusions it is stated that VGI contributed significantly to the tracking and provided immediate and in-depth information, crucial for prevention, mitigation, preparedness, and response tasks of the DM cycle. In addition, they stated that by using VGI, the enhancement of their emergency situation awareness can lead to better decisions in planning operations for giving aid, not

The above assumption was verified in similar research [48], regarding the Colorado floods, occurring in the United States in 2013. The significance of correctly tracking all the phases of a natural disastrous event emerged, completely documenting that the negative impact of similar flood events that may potentially occur in the future can be minimized. Moreover, VGI data sources were able to fill an important gap of information regarding the floods, especially since the flood

tweets and, sequentially, identifying flood events in the geo-parsed content.

In terms of flood identification, in [39] a Twitter corpus consisting of 87.6 million tweets was analyzed, leading to the identification of 10.000 flood events, globally. The main steps of methodology applied and included initially geo-referencing of the

**2. DM and VGI**

pragmatic guidance."

flood events.

**160**

concluded.

As stated in previous sections, the general notion of VGI is not strictly related to digital data procedures but also highly related to participatory approaches. After all, community involvement has been emerged as an important part of the DM operational activities, as by imbuing the community with a sense of ownership of the risk reduction process, resilience to deal with natural hazards is increased [19]. Moreover those approaches can be proven vital, especially in developing countries, which are expected to confront with the major consequences of the climate change, despite their minimum contribution to the problem [55], while data availability in many cases is affected, due to laws, security protocols, illiteracy, cultural barriers, and economic reasons [31]. In addition, the budget needed for organizing can be minimized by engaging local authorities to provide premises and by using open-source software solutions [56] for collecting and processing information related to floods.

An interesting approach was presented in [22] who refer to the Chametla community located in Baja California that aimed to reduce the risk of negative consequences in the event of a potential flood occurrence in Baja California, Mexico. The community received appropriate training by experts. In specific, they organized a workshop, in Chametla, in which the participants were able to annotate on printed satellite imagery their property along with various spots of the area that are considered vulnerable to floods, building thus a related map. Sequentially, they presented their results, and upon related discussions, they were able to correct and adjust various spots on the map. The output was reviewed by risk management experts who provided additional corrections. The final map was created by a GIS technician who digitally mapped all the printed information. The workshop participants created an ordered list of tasks that they could do in order to minimize the area's vulnerability to the floods. Those tasks included, among others, the pavement of few streets and the creation of drainage. In addition they distributed surveys for collecting socioeconomic and flood awareness level information of the locals. They concluded that the majority of the inhabitants are taking measures for being protected in the event of a hurricane or other similar disastrous event.

A similar approach had been presented in [57] who introduced a methodology, for exploring the potentials of joined activities of scientific teams and locals. They used two case studies, the Upper Danube and the Upper Brahmaputra river basins, while the aim of the participatory activities was to assimilate local knowledge in scientific flood event management procedures for mitigating potential disaster in mountain areas. They organized two related workshops, one for each case study, in which the participants, entitled as local actors (LAs), received training, in a story telling mode, regarding the climate change and its potential consequences in the next 40 years. Sequentially they were invited to evaluate proposed response tasks by defining and prioritizing criteria, according to their local knowledge. The output was processed by subject matter experts and was assimilated in related strategies for coping with flooding.

A community though may not be solely consisted of locals. In [21] an innovative participatory approach was presented, linked to the decision-making for prevention, preparedness, and mitigation tasks of flood events. In specific, a community was created, consisting of more than 117 Brazilian Scientists and flood subject matter experts from NGOs and private companies. As case studies, the municipalities of Lajeado and Estrela, located in South Brazil, were used. In those areas, mostly due to the geo-morphological characteristics, floods occur frequently, sometimes twice per year. The expert community was asked to define the most suitable criteria that define an area as vulnerable to floods. The feedback was received through the distribution of related questionnaires. Sequentially, the criteria were ranked according to their level of importance with the use of two related processes: the analytic hierarchic process (AHP) and the analytic network process (ANP). Finally, by using GIS and mp algebra, they created related maps that indicate the areas most vulnerable to floods according to the output of each ranking process.

#### **3.2 Combined approaches**

Apart from pure VGI-related activities, there is a lot of published research that tends to combine VGI along with a plethora of other data sources, creating thus the so-called mashups [58] which act complementary to each other aspiring to have the most efficient output. Those mashups consist of VGI data along with imagery, authoritative data, and ground-truth observations and measurements.

In specific, in [43] a hybrid approach was presented, manipulating flood-related data extracted from social networks and data gathered from a graphics processing unit (GPU) for accelerated hydrodynamic modeling. The approach was assessed in two flood events of the Tyne and Wear floods which occurred in June and August 2012, respectively, in the United Kingdom. About 1800 and 160 tweets were collected for each flood, respectively, while 43 and 13 tweets met the defined criteria for assimilation to related inundation models.

In [19] a method for implementing VGI in flood forecasting and mapping activities was presented. In specific, information through YouTube and through data collected by applying various queries in Twitter and various other Internet searches was extracted. The volume of extracted information that was assimilated in their flood-related models was small (~20 videos in YouTube, lack of related data in Twitter).

The output of the research presented in [18, 43] emerges the contribution of VGI data to calibrating inundation models, rises though challenges for assimilating effectively large volume of produced VGI information in related models.

**163**

*Fundamentals of Volunteered Geographic Information in Disaster Management Related to Floods*

Apart from methodologies and approaches for manipulating VGI data for DM of flood events, there is published research indicating the development of

In [19] a novel participatory platform for engaging communities in all aspects of the flooding life cycle, entitled "NOAH," was introduced. The approach was applied in biosphere reserves, recognized by UNESCO. Definitely the app is associated to

In particular the users of the platform are divided into two specific categories: the anonymous users, who make contributions without providing any personal information, and the registered ones, who share observations in a more authenticated way. While sharing observations the users are requested to classify the reported observations in predefined categories. Various validation rules of the system focus on increasing the quality of the shared information. Those rules include, i.e., the mandatory presence of GPS coordinates in each uploaded photo, while post-processing procedures are applied on the shared information. The collected data are used for assessing and calibrating an inundation model, by validating or adjusting the water level according to a geo-tagged photo. Finally they assessed the usability of their platform by distributing questionnaires to the users. The feedback gained was that their platform is at an above-average level in terms of usability, while a general assumption was that VGI can contribute to mitigating a flood event occurrence and to providing information for adjusting

In [59] a collaborative mapping approach was presented, based on the Ushahidi platform, through which ordinary people shared flood-related observations by using their mobile devices. The observations indicated points with measurements regarding the flood levels in various parts of Sao Paolo, Brazil. Among the conclusions of the research is the difficulty in engaging citizens to report to the platform. Moreover, by distributing questionnaires, feedback was collected regarding the app's usability and the data reliability. The main findings were that an improved

**4. Open challenges of manipulating VGI data for effective DM of flood** 

In the current section, the author addresses the open challenges of VGI data sources when those are utilized for DM purposes, related to floods. The open challenges are accumulated to four main clusters, all blended by the general notion of quality: (a) classification, (b) geo-referencing, (c) visualization, and (d) automation. In the following paragraphs, each cluster is analyzed thoroughly.

The first set of challenges is related to dividing the ones related to flood information into the proper categories. A complete and proper classification structure could lead to extract information that can give valuable insights in various phases of a flood event occurrence. Various classification structures have been presented [42, 48, 54, 57–61]. A conclusion though to an essential, commonly used, classification can be proven beneficial for advancing the general research to a next step. In **Table 1** the author suggests a conceptual classification structure, consisting of 12

user interface of the app, would be significant for user engagement.

**3.3 Developed web applications that utilize VGI for flood events**

*DOI: http://dx.doi.org/10.5772/intechopen.92225*

the conventional type of VGI sources.

web applications.

inundation models.

**events**

**4.1 Classification**

main categories.

*Fundamentals of Volunteered Geographic Information in Disaster Management Related to Floods DOI: http://dx.doi.org/10.5772/intechopen.92225*

#### **3.3 Developed web applications that utilize VGI for flood events**

Apart from methodologies and approaches for manipulating VGI data for DM of flood events, there is published research indicating the development of web applications.

In [19] a novel participatory platform for engaging communities in all aspects of the flooding life cycle, entitled "NOAH," was introduced. The approach was applied in biosphere reserves, recognized by UNESCO. Definitely the app is associated to the conventional type of VGI sources.

In particular the users of the platform are divided into two specific categories: the anonymous users, who make contributions without providing any personal information, and the registered ones, who share observations in a more authenticated way. While sharing observations the users are requested to classify the reported observations in predefined categories. Various validation rules of the system focus on increasing the quality of the shared information. Those rules include, i.e., the mandatory presence of GPS coordinates in each uploaded photo, while post-processing procedures are applied on the shared information. The collected data are used for assessing and calibrating an inundation model, by validating or adjusting the water level according to a geo-tagged photo. Finally they assessed the usability of their platform by distributing questionnaires to the users. The feedback gained was that their platform is at an above-average level in terms of usability, while a general assumption was that VGI can contribute to mitigating a flood event occurrence and to providing information for adjusting inundation models.

In [59] a collaborative mapping approach was presented, based on the Ushahidi platform, through which ordinary people shared flood-related observations by using their mobile devices. The observations indicated points with measurements regarding the flood levels in various parts of Sao Paolo, Brazil. Among the conclusions of the research is the difficulty in engaging citizens to report to the platform. Moreover, by distributing questionnaires, feedback was collected regarding the app's usability and the data reliability. The main findings were that an improved user interface of the app, would be significant for user engagement.

## **4. Open challenges of manipulating VGI data for effective DM of flood events**

In the current section, the author addresses the open challenges of VGI data sources when those are utilized for DM purposes, related to floods. The open challenges are accumulated to four main clusters, all blended by the general notion of quality: (a) classification, (b) geo-referencing, (c) visualization, and (d) automation. In the following paragraphs, each cluster is analyzed thoroughly.

#### **4.1 Classification**

The first set of challenges is related to dividing the ones related to flood information into the proper categories. A complete and proper classification structure could lead to extract information that can give valuable insights in various phases of a flood event occurrence. Various classification structures have been presented [42, 48, 54, 57–61]. A conclusion though to an essential, commonly used, classification can be proven beneficial for advancing the general research to a next step. In **Table 1** the author suggests a conceptual classification structure, consisting of 12 main categories.

*Flood Impact Mitigation and Resilience Enhancement*

strategies for coping with flooding.

**3.2 Combined approaches**

floods according to the output of each ranking process.

for assimilation to related inundation models.

of few streets and the creation of drainage. In addition they distributed surveys for collecting socioeconomic and flood awareness level information of the locals. They concluded that the majority of the inhabitants are taking measures for being

A similar approach had been presented in [57] who introduced a methodology, for exploring the potentials of joined activities of scientific teams and locals. They used two case studies, the Upper Danube and the Upper Brahmaputra river basins, while the aim of the participatory activities was to assimilate local knowledge in scientific flood event management procedures for mitigating potential disaster in mountain areas. They organized two related workshops, one for each case study, in which the participants, entitled as local actors (LAs), received training, in a story telling mode, regarding the climate change and its potential consequences in the next 40 years. Sequentially they were invited to evaluate proposed response tasks by defining and prioritizing criteria, according to their local knowledge. The output was processed by subject matter experts and was assimilated in related

A community though may not be solely consisted of locals. In [21] an innovative participatory approach was presented, linked to the decision-making for prevention, preparedness, and mitigation tasks of flood events. In specific, a community was created, consisting of more than 117 Brazilian Scientists and flood subject matter experts from NGOs and private companies. As case studies, the municipalities of Lajeado and Estrela, located in South Brazil, were used. In those areas, mostly due to the geo-morphological characteristics, floods occur frequently, sometimes twice per year. The expert community was asked to define the most suitable criteria that define an area as vulnerable to floods. The feedback was received through the distribution of related questionnaires. Sequentially, the criteria were ranked according to their level of importance with the use of two related processes: the analytic hierarchic process (AHP) and the analytic network process (ANP). Finally, by using GIS and mp algebra, they created related maps that indicate the areas most vulnerable to

Apart from pure VGI-related activities, there is a lot of published research that tends to combine VGI along with a plethora of other data sources, creating thus the so-called mashups [58] which act complementary to each other aspiring to have the most efficient output. Those mashups consist of VGI data along with imagery,

In specific, in [43] a hybrid approach was presented, manipulating flood-related data extracted from social networks and data gathered from a graphics processing unit (GPU) for accelerated hydrodynamic modeling. The approach was assessed in two flood events of the Tyne and Wear floods which occurred in June and August 2012, respectively, in the United Kingdom. About 1800 and 160 tweets were collected for each flood, respectively, while 43 and 13 tweets met the defined criteria

In [19] a method for implementing VGI in flood forecasting and mapping activities was presented. In specific, information through YouTube and through data collected by applying various queries in Twitter and various other Internet searches was extracted. The volume of extracted information that was assimilated in their flood-related models

The output of the research presented in [18, 43] emerges the contribution of VGI data to calibrating inundation models, rises though challenges for assimilating

authoritative data, and ground-truth observations and measurements.

was small (~20 videos in YouTube, lack of related data in Twitter).

effectively large volume of produced VGI information in related models.

protected in the event of a hurricane or other similar disastrous event.

**162**


*\*The following terms are analyzed in detail in Tables 2–5: Effects on social life in Table 3; info related to consequences in Table 2; emotions expressed as a result of the consequences of the flood in Table 5; and flood aid in Table 4.*

#### **Table 1.**

*Initial level of classification.*

By adopting the basic principles of a classification schema like the one proposed, a researcher can receive, as output, a high level of specialized information which is vital for contributing efficiently to various phases of the DM cycle.

Moreover, by further sub-classifying categories of the initial classification structure, formed specialized information, regarding a flood event, can be extracted (i.e., **Tables 2**–**5**). In **Table 2** a consequence-measurement scale ranging from I to V is proposed. The scale has an acceding logic in terms of the impact of the consequences, starting from value I, which is associated to simple identification of a rain or storm, up to value V, which is linked solely to human loss.

Similar quantification logic is applied in **Table 3** regarding the effects on social life, while in this case Value I is related to the minor impact of a rain and Value V is related to zero social activity.

Finally, **Tables 4** and **5** subdivide the information related to flood aid and expressed emotions, respectively. Three main types are defined for each main classification category.

#### **4.2 Geo-referencing**

The second cluster of challenges is related to correct and precise geo-referencing of the information, as the only way to have accurate maps is to have sufficient geo-referencing of the data. This vital set of challenges has a lot of complex characteristics that need to be taken into account, especially while processing specific sets of data mostly linked to unconventional VGI data sources like texts posted through social networks.


**165**

*Fundamentals of Volunteered Geographic Information in Disaster Management Related to Floods*

III Meetings, exhibitions, and events are canceled due to the storm. People cannot

V Zero social activity: people stay at homes or at places in which they are protected

D. Places in which flood victims can register the damages provoked from the flood event

I Almost zero effect. People may just need to have an umbrella II People are afraid to commute and travel because of the weather

IV The majority of services, stores, and companies stop

from the flood

F. Spots in which food and clothing supplies are gathered

move

**Description**

occurrence V. General volunteered actions

**Description**

S Sadness for damages/human loss

There are some social media that include location-related info in their semantics.

Indicatively, Twitter has the ability to embed x and y coordinates of the spot in which a post is published (geo-located tweets). However, the percentage of those tweets against the total sum varies from 1 to 5% [62–64]. Moreover, as various researchers have stated, the geographic place in which a post was published is not

H Happy emotions for a successful mitigation of a negative consequence

An effective way to cope with this is to detect geographic entities that appear within each tweet's text. Even if there are various issues in this approach as well though, mostly regarding the presence of more than one geo-locations and more than one flood-related observations in a single text, the quantity of geo-referenced information extracted is significantly higher. Various geo-validation rules based on filtering the observation according to its distance from the flood event occurrence may solve the problem partially, while applied artificial intelligence for clearing

There are various algorithms, published in the international literature, that manipulate text corpuses from social media in order to detect geo-locations. One of those is the TAGG algorithm [66] which is based on detecting geo-locations in a text, using a database of known locations. The author has also presented techniques that aspire to contribute to effective geo-referencing of DM-related information [67].

necessarily associated to the descriptive information of a tweet's text [29].

ambiguity is also an interesting approach [65].

*DOI: http://dx.doi.org/10.5772/intechopen.92225*

**Effect score value Description**

*Quantification of effects on social life.*

**Table 3.**

**Table 4.**

**Table 5.**

**Categories of flood aid**

*Sub-classification of flood aid.*

*Sub-classification of emotions.*

SL Solidarity

**Categories of emotions**

**Table 2.**

*Quantification of consequence score values.*

*Fundamentals of Volunteered Geographic Information in Disaster Management Related to Floods DOI: http://dx.doi.org/10.5772/intechopen.92225*


#### **Table 3.**

*Flood Impact Mitigation and Resilience Enhancement*

**Main classification structure: categories**

Irony expressed due to lack of effective flood

Self-organizing of volunteers for flood-related

management

purposes

*Initial level of classification.*

*Table 4.*

**Table 1.**

By adopting the basic principles of a classification schema like the one proposed, a researcher can receive, as output, a high level of specialized information which is

the flood\*

Flood modeling information

Moreover, by further sub-classifying categories of the initial classification structure, formed specialized information, regarding a flood event, can be extracted (i.e., **Tables 2**–**5**). In **Table 2** a consequence-measurement scale ranging from I to V is proposed. The scale has an acceding logic in terms of the impact of the consequences, starting from value I, which is associated to simple identification

Identification of rain/storm Info related to DM (including prevention)

*\*The following terms are analyzed in detail in Tables 2–5: Effects on social life in Table 3; info related to consequences in Table 2; emotions expressed as a result of the consequences of the flood in Table 5; and flood aid in* 

Emotions expressed as a result of the consequences of

Identification of flood Info related to Consequences\*

Effects on social life\* Situation overview Weather-related information Flood aid\*

Similar quantification logic is applied in **Table 3** regarding the effects on social life, while in this case Value I is related to the minor impact of a rain and Value V is

The second cluster of challenges is related to correct and precise geo-referencing of the information, as the only way to have accurate maps is to have sufficient geo-referencing of the data. This vital set of challenges has a lot of complex characteristics that need to be taken into account, especially while processing specific sets of data mostly linked to unconventional VGI data sources like texts

III Damages, problems in the traffic network, minor human injuries, flooded streets,

IV Huge damages, missing people, homeless people, serious danger to human life,

airport, school, or other public premises are closed; help tickets to fire brigade

to flood, isolated hamlets, and people that cannot escape a premise

emergency situation, no electricity or water at a city level. Busses change track due

Finally, **Tables 4** and **5** subdivide the information related to flood aid and expressed emotions, respectively. Three main types are defined for each main

vital for contributing efficiently to various phases of the DM cycle.

of a rain or storm, up to value V, which is linked solely to human loss.

related to zero social activity.

posted through social networks.

**Consequence score Description**

V Loss of human life

*Quantification of consequence score values.*

I Simple identification of rain or storm II Torrential storm, human fear, terror

classification category.

**4.2 Geo-referencing**

**164**

**Table 2.**

*Quantification of effects on social life.*


#### **Table 4.** *Sub-classification of flood aid.*


#### **Table 5.**

*Sub-classification of emotions.*

There are some social media that include location-related info in their semantics. Indicatively, Twitter has the ability to embed x and y coordinates of the spot in which a post is published (geo-located tweets). However, the percentage of those tweets against the total sum varies from 1 to 5% [62–64]. Moreover, as various researchers have stated, the geographic place in which a post was published is not necessarily associated to the descriptive information of a tweet's text [29].

An effective way to cope with this is to detect geographic entities that appear within each tweet's text. Even if there are various issues in this approach as well though, mostly regarding the presence of more than one geo-locations and more than one flood-related observations in a single text, the quantity of geo-referenced information extracted is significantly higher. Various geo-validation rules based on filtering the observation according to its distance from the flood event occurrence may solve the problem partially, while applied artificial intelligence for clearing ambiguity is also an interesting approach [65].

There are various algorithms, published in the international literature, that manipulate text corpuses from social media in order to detect geo-locations. One of those is the TAGG algorithm [66] which is based on detecting geo-locations in a text, using a database of known locations. The author has also presented techniques that aspire to contribute to effective geo-referencing of DM-related information [67].

Particularly, regarding the latter, a precision score level is indicated for each georeference (**Table 6**).

According to the precision level of each geo-reference, the output of the processed information can be used from authorities (precision at a city level) or from rescue teams and locals (precision at a street level). Effective geo-referencing for DM related to floods needs is still quite a challenging sub-topic, especially towards the goal of high precision.

## **4.3 Visualization**

The third cluster of challenges is linked to generating appropriate visualization results. In specific, the generated maps and graphs must be readable to people that could potentially be stakeholders of the DM cycle but with zero knowledge regarding geography and science in general. The production of complicated schemas, as an output of a bright methodology, is often the reason of not widening a methodology to all DM levels, as the complexity through which the information is delivered to the recipients limits the capability of having a crucial message understood. Even if we are living in an age that the literacy levels are higher than ever, geographical literacy is still a challenge for a plethora of people globally. Within this framework, some visualization suggestions can be found in **Figures 1, 2**.

**Figure 1** displays information related to the consequences of a flood event, occurring in West Attica, Greece. Each bullet located on the maps represents a consequence score value (**Table 2**). Since both flood events caused human losses, there are many bullets in red.

Furthermore, **Figure 2** visualizes the frequency of posted tweets that are related to identification of rain. With those maps an initial assumption may be provided to the DM stakeholders, regarding the potentials of flood occurrence, especially in the areas in which the frequency of tweets, indicating a rain, is comparatively significantly higher than in other areas.

#### **4.4 Automation**

Finally, the fourth cluster of challenges is related to automation. As many researchers agree, VGI data analysis is a time-consuming process [46, 61, 68–69]. Especially when dealing with unconventional sources, the volume of produced information may consist of hundred thousands or even millions of data-rows. Techniques, like natural language processing (NLP), designated for handling large amount of information provide effective solutions. Moreover, the use of artificial intelligence applications, for classifying the related content, such as support vector machines, can radically reduce the time needed for classifying the information and for coping with ambiguities. Published research that employs classifiers provides really promising results [53, 70].


**167**

**5. Conclusions**

**Figure 2.**

**Figure 1.**

*Fundamentals of Volunteered Geographic Information in Disaster Management Related to Floods*

The main aim of this chapter was to inform the reader about the fundamentals regarding VGI and its applications to DM of flood events. In previous sections, the

*Volume of produced rain-related information during the devastating floods of West Attica, 2017 [52].*

*DOI: http://dx.doi.org/10.5772/intechopen.92225*

*Flood consequences in Mandra, West Attica, Greece [52].*

**Table 6.**

*Geographic precision score values of geo-located posts.*

*Fundamentals of Volunteered Geographic Information in Disaster Management Related to Floods DOI: http://dx.doi.org/10.5772/intechopen.92225*

#### **Figure 1.**

*Flood Impact Mitigation and Resilience Enhancement*

visualization suggestions can be found in **Figures 1, 2**.

reference (**Table 6**).

**4.3 Visualization**

the goal of high precision.

there are many bullets in red.

cantly higher than in other areas.

really promising results [53, 70].

**Geographic precision score values Description**

II Street name

IV Municipality V Prefecture and above

*Geographic precision score values of geo-located posts.*

III Neighborhood or hamlet

I Street name and number or specific POIS

**4.4 Automation**

Particularly, regarding the latter, a precision score level is indicated for each geo-

According to the precision level of each geo-reference, the output of the processed information can be used from authorities (precision at a city level) or from rescue teams and locals (precision at a street level). Effective geo-referencing for DM related to floods needs is still quite a challenging sub-topic, especially towards

The third cluster of challenges is linked to generating appropriate visualization results. In specific, the generated maps and graphs must be readable to people that could potentially be stakeholders of the DM cycle but with zero knowledge regarding geography and science in general. The production of complicated schemas, as an output of a bright methodology, is often the reason of not widening a methodology to all DM levels, as the complexity through which the information is delivered to the recipients limits the capability of having a crucial message understood. Even if we are living in an age that the literacy levels are higher than ever, geographical literacy is still a challenge for a plethora of people globally. Within this framework, some

**Figure 1** displays information related to the consequences of a flood event, occurring in West Attica, Greece. Each bullet located on the maps represents a consequence score value (**Table 2**). Since both flood events caused human losses,

Finally, the fourth cluster of challenges is related to automation. As many researchers agree, VGI data analysis is a time-consuming process [46, 61, 68–69]. Especially when dealing with unconventional sources, the volume of produced information may consist of hundred thousands or even millions of data-rows. Techniques, like natural language processing (NLP), designated for handling large amount of information provide effective solutions. Moreover, the use of artificial intelligence applications, for classifying the related content, such as support vector machines, can radically reduce the time needed for classifying the information and for coping with ambiguities. Published research that employs classifiers provides

Furthermore, **Figure 2** visualizes the frequency of posted tweets that are related to identification of rain. With those maps an initial assumption may be provided to the DM stakeholders, regarding the potentials of flood occurrence, especially in the areas in which the frequency of tweets, indicating a rain, is comparatively signifi-

**166**

**Table 6.**

*Flood consequences in Mandra, West Attica, Greece [52].*

#### **Figure 2.**

*Volume of produced rain-related information during the devastating floods of West Attica, 2017 [52].*

## **5. Conclusions**

The main aim of this chapter was to inform the reader about the fundamentals regarding VGI and its applications to DM of flood events. In previous sections, the author described the general notion of VGI and the similar terms that can be found in the international literature and provided awareness of its basic characteristics and properties. Sequentially, significant research related to VGI and flood event management was presented. Considering the above, it can be safely assumed that VGI can effectively be used for identifying flood events and for documenting various phases of the unfoldness along with the tracking of the negative consequences and tasks crucial for the preparedness against similar flood events that may potentially occur. Moreover, the use of VGI provides significant assistance in calibrating and validating flood and inundation models, by providing specific spatiotemporal information. Furthermore, participatory activities can provide significant contribution regarding preparedness by identifying vulnerable spots and performing adjustments in the urban environment, making thus an area more resilient to floods. Similar activities consisting of subject matter experts can provide valuable support in the decision-making processes of the DM related to flood management.

Regarding data availability, the unconventional VGI data sources provide an enormous volume of information related to floods; information though with anarchic characteristics surely is not compliant to specifications, while the conventional VGI data sources, which are usually purpose-driven, may provide data more compatible to the DM needs; the data production though is limited.

The open challenges of VGI data, when those are manipulated for DM purposes, are accumulated in a set of four clusters. The first cluster is related to classification. The more complete and detailed classification structure, the more specialized the processed information will become. Precise geo-referencing; effective and simplified visualization of the processed information, easily readable by all the DM stakeholders; and finally adaptation of automation techniques complete the set of the challenges.

Assuming that the social networks will continue to be evolved and enlarged, it is expected that methodologies that will be able to assimilate all the potentials of VGI in the DM mechanisms will be more and more dominant.

#### **Conflict of interest**

The author declares no conflict of interest.

#### **Author details**

Stathis G. Arapostathis Harokopio University, Athens, Greece

\*Address all correspondence to: sarapos@hua.gr

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**169**

*Fundamentals of Volunteered Geographic Information in Disaster Management Related to Floods*

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[14] Sheth A. Citizen sensing, social signals, and enriching human

[15] Foody GM, See L, Fritz S, Van der Velde M, Perger C, Schill C, et al. Assessing the accuracy of volunteered geographic information arising from multiple contributors to an internet based collaborative project. Transactions in GIS. 2013;**17**(6):847-860

[16] Halem M, Yesha Y, Aulov O, Martineau J, Brown S, Conte T. Collaborative Science: Human Sensor Networks for Real-time Natural Disaster

Prediction. In: AGU Fall Meeting

[17] Aulov O, Price A, Halem M. AsonMaps: A platform for aggregation visualization and analysis of disaster related human sensor network observations. In: ISCRAM; 2014

[18] Annis A, Nardi F. Integrating VGI and 2D hydraulic models into a

experience. IEEE Internet Computing.

pp. 31-42

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Knowledge. Dordrecht: Springer; 2013.

[11] Irwin A. Citizen Science: A Study of People, Expertise and Sustainable Development. Sussex, UK: Psychology

Information: Overview and Typology of Participation. In: Crowdsourcing Geographic Knowledge. Dordrecht:

[13] Kullenberg C, Kasperowski D. What is citizen science?–a scientometric meta-

*DOI: http://dx.doi.org/10.5772/intechopen.92225*

[1] Goodchild MF. Citizens as sensors: The world of volunteered geography. GeoJournal. 2007;**69**(4):211-221

[2] Sieber R. Geoweb for social change. USA: National Center for Geographic Information & Analysis Santa Barbara;

[3] Bishr M, Mantelas L. A trust and reputation model for filtering and classifying knowledge about urban growth. GeoJournal. 2008;**72**(3-4):229-237

[4] Degrossi LC, de Albuquerque JP, Fava MC, Mendiondo EM. Flood citizen observatory: A crowdsourcing-based approach for flood risk management in Brazil. International Conference on Software Engineering and Knowledge Engineering. SEKE. 2014:570-575

[5] Miorandi D, Carreras I, Gregori E, Graham I, Stewart J. Measuring net neutrality in mobile internet: Towards a crowdsensing-based citizen observatory. In: 2013 IEEE international conference on communications workshops (ICC).

IEEE; 2013. pp. 199-203

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[8] Elwood S. Critical issues in participatory GIS: Deconstructions, reconstructions, and new research directions. Transactions in GIS.

[9] Antoniou V, Morley J, Haklay M.

Web 2.0 geotagged photos: Assessing the spatial dimension of the phenomenon. Geomatica.

2006;**10**(5):693-708

2010;**64**(1):99-110

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*Fundamentals of Volunteered Geographic Information in Disaster Management Related to Floods DOI: http://dx.doi.org/10.5772/intechopen.92225*

## **References**

*Flood Impact Mitigation and Resilience Enhancement*

author described the general notion of VGI and the similar terms that can be found in the international literature and provided awareness of its basic characteristics and properties. Sequentially, significant research related to VGI and flood event management was presented. Considering the above, it can be safely assumed that VGI can effectively be used for identifying flood events and for documenting various phases of the unfoldness along with the tracking of the negative consequences and tasks crucial for the preparedness against similar flood events that may potentially occur. Moreover, the use of VGI provides significant assistance in calibrating and validating flood and inundation models, by providing specific spatiotemporal information. Furthermore, participatory activities can provide significant contribution regarding preparedness by identifying vulnerable spots and performing adjustments in the urban environment, making thus an area more resilient to floods. Similar activities consisting of subject matter experts can provide valuable support

in the decision-making processes of the DM related to flood management.

compatible to the DM needs; the data production though is limited.

in the DM mechanisms will be more and more dominant.

The author declares no conflict of interest.

Regarding data availability, the unconventional VGI data sources provide an enormous volume of information related to floods; information though with anarchic characteristics surely is not compliant to specifications, while the conventional VGI data sources, which are usually purpose-driven, may provide data more

The open challenges of VGI data, when those are manipulated for DM purposes, are accumulated in a set of four clusters. The first cluster is related to classification. The more complete and detailed classification structure, the more specialized the processed information will become. Precise geo-referencing; effective and simplified visualization of the processed information, easily readable by all the DM stakeholders; and finally adaptation of automation techniques complete the set of the challenges. Assuming that the social networks will continue to be evolved and enlarged, it is expected that methodologies that will be able to assimilate all the potentials of VGI

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

**168**

**Author details**

Stathis G. Arapostathis

**Conflict of interest**

Harokopio University, Athens, Greece

provided the original work is properly cited.

\*Address all correspondence to: sarapos@hua.gr

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[2] Sieber R. Geoweb for social change. USA: National Center for Geographic Information & Analysis Santa Barbara; 2007;**3**:2008

[3] Bishr M, Mantelas L. A trust and reputation model for filtering and classifying knowledge about urban growth. GeoJournal. 2008;**72**(3-4):229-237

[4] Degrossi LC, de Albuquerque JP, Fava MC, Mendiondo EM. Flood citizen observatory: A crowdsourcing-based approach for flood risk management in Brazil. International Conference on Software Engineering and Knowledge Engineering. SEKE. 2014:570-575

[5] Miorandi D, Carreras I, Gregori E, Graham I, Stewart J. Measuring net neutrality in mobile internet: Towards a crowdsensing-based citizen observatory. In: 2013 IEEE international conference on communications workshops (ICC). IEEE; 2013. pp. 199-203

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[9] Antoniou V, Morley J, Haklay M. Web 2.0 geotagged photos: Assessing the spatial dimension of the phenomenon. Geomatica. 2010;**64**(1):99-110

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[24] Arapostathis S. The social media network twitter as a source of volunteered geographic information for the development of seismic intensity maps. Doctoral dissertation. Harokopio University. School of the Environment, Geography and Applied Economics, department of Geography. 2015. Available from aahttp://hdl.handle. net/10442/hedi/36547

[25] Neis P, Zielstra D, Zipf A. The street network evolution of crowdsourced maps: OpenStreetMap in Germany 2007-2011. Future Internet. 2012;**4**(1):1-21

[26] Neis P, Zipf A. Analyzing the contributor activity of a volunteered geographic information project—The case of OpenStreetMap. ISPRS International Journal of Geo-Information. 2012;**1**(2):146-165

[27] Eckle M, de Albuquerque JP, Herfort B, Zipf A, Leiner R, Wolff R, et al. Leveraging OpenStreetMap to Support Flood Risk Management in Municipalities: A Prototype Decision Support System. In: ISCRAM; 2016

[28] Corbane C, Lemoine G, Kauffmann M. Relationship between the spatial distribution of SMS messages reporting needs and building damage in 2010 Haiti disaster. Natural Hazards and Earth System Sciences. 2012;**12**(2):255-265

[29] Huiji G, Barbier G. Harnessing the crowdsourcing power of social media for disaster relief. IEEE Intelligent Systems. 2011;**26**(3):1541-1672

[30] Poushter J, Bishop C, Chwe H. Social Media Use Continues to Rise in Developing Countries but Plateaus Across Developed Ones. Washinghton DC, USA: Pew Research Center; 2018. p. 22

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[42] Yin J, Lampert A, Cameron M, Robinson B, Power R. Using social media to enhance emergency situation awareness. IEEE Computer Society.

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Francis Group; 2019

2012;**27**(6):52-59

2017;**10**(3):370-380

2015;**6**(2):200-201

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pp. 142-154

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[45] Whittaker J, McLennan B, Handmer J. A review of informal volunteerism in emergencies and disasters: Definition, opportunities and challenges. International Journal of Disaster Risk Reduction.

[46] Arapostathis SG. Tweeting about floods of Messinia (Greece, September 2016)-towards a credible methodology for disaster management purposes. In: International Conference on Information Technology in Disaster Risk Reduction. Cham: Springer; 2018.

[47] McDougall K. Using volunteered geographic information to map the Queensland floods. In: Proceedings of the Surveying & Spatial Sciences Biennial Conference. Wellington, New

Zealand: Scion; 2011. pp. 21-25

[48] Dashti S, Palen L, Heris MP, Anderson KM, Anderson S, Anderson TJ. Supporting disaster

*DOI: http://dx.doi.org/10.5772/intechopen.92225*

assessment of the French OpenStreetMap

13th AGILE International Conference on Geographic Information Science,

Giomaraes, Portugal; 2010

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dataset. Transactions in GIS.

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Springer; 2014. pp. 242-250

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[37] Bovet A, Makse HA. Influence of fake news in twitter during the 2016 US presidential election. Nature Communications. 2019;**10**(1):1-14

[38] Elwood S, Goodchild MF, Sui D. Prospects for VGI research and the emerging fourth paradigm. In: Crowdsourcing Geographic

pp. 361-375

Knowledge. Dordrecht: Springer; 2013.

[39] de Bruijn JA, de Moel H, Jongman B, de Ruiter MC, Wagemaker J, Aerts JC. A global database of historic and real-time flood events based on social media. Scientific Data. 2019;**6**(1):1-12

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[34] Napolitano M, Mooney P. MVP OSM: A tool to identify areas of high quality contributor activity in OpenStreetMap. The Bulletin of the Society of Cartographers.

2010;**14**(4):435-459

2012;**45**(1):10-18

*Fundamentals of Volunteered Geographic Information in Disaster Management Related to Floods DOI: http://dx.doi.org/10.5772/intechopen.92225*

13th AGILE International Conference on Geographic Information Science, Giomaraes, Portugal; 2010

*Flood Impact Mitigation and Resilience Enhancement*

[25] Neis P, Zielstra D, Zipf A. The street network evolution of crowdsourced maps: OpenStreetMap in Germany 2007-2011. Future Internet.

[26] Neis P, Zipf A. Analyzing the contributor activity of a volunteered geographic information project—The case of OpenStreetMap. ISPRS International Journal of Geo-Information. 2012;**1**(2):146-165

[27] Eckle M, de Albuquerque JP, Herfort B, Zipf A, Leiner R, Wolff R, et al. Leveraging OpenStreetMap to Support Flood Risk Management in Municipalities: A Prototype Decision Support System. In: ISCRAM; 2016

[28] Corbane C, Lemoine G,

2012;**12**(2):255-265

[30] Poushter J, Bishop C,

Center; 2018. p. 22

Manchester; 2011

Kauffmann M. Relationship between the spatial distribution of SMS

messages reporting needs and building damage in 2010 Haiti disaster. Natural Hazards and Earth System Sciences.

[29] Huiji G, Barbier G. Harnessing the crowdsourcing power of social media for disaster relief. IEEE Intelligent Systems. 2011;**26**(3):1541-1672

Chwe H. Social Media Use Continues to Rise in Developing Countries but Plateaus Across Developed Ones. Washinghton DC, USA: Pew Research

[31] Yap NT. In: Heeks R, Ospina A, editors. for IDRCDisaster Management, Developing Country Communities & Climate Change: The Role of ICTs. Manchester: Centre for Development Informatics, Institute for Development Policy and Management, University of

[32] Brando C, Bucher B. Quality in user generated spatial content: A matter of specifications. In: proceedings of the

2012;**4**(1):1-21

data assimilation framework for real time flood forecasting and mapping. Geo-spatial Information Science.

[19] O'Grady MJ, Evans B, Eigbogba S, Muldoon C, Campbell AG, Brewer PA, et al. Supporting participative pre-flood risk reduction in a UNESCO biosphere. Journal of Flood Risk Management.

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Earth System Sciences. 2018;**22**(1):373-390

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Almoradie ADS.Participatory flood vulnerability assessment: A multicriteria approach. Hydrology and

[22] Cruz-Bello GM, Alfie-Cohen M,

Larralde-Corona AH, Perez JR. Flood vulnerability reduction, using a partial participatory GIS approach. A study case in Baja California Sur, Mexico. International archives of the photogrammetry, remote sensing and spatial information sciences. 2018;

[23] Sui DZ. The wikification of GIS and its consequences: Or Angelina Jolie's new tattoo and the future of GIS. Computers, Environment and Urban Systems. 2008;**1**(32):1-5

[24] Arapostathis S. The social media network twitter as a source of volunteered geographic information for the development of seismic intensity maps. Doctoral dissertation. Harokopio University. School of the Environment, Geography and Applied Economics, department of Geography. 2015. Available from aahttp://hdl.handle.

net/10442/hedi/36547

**170**

[33] Girres JF, Touya G. Quality assessment of the French OpenStreetMap dataset. Transactions in GIS. 2010;**14**(4):435-459

[34] Napolitano M, Mooney P. MVP OSM: A tool to identify areas of high quality contributor activity in OpenStreetMap. The Bulletin of the Society of Cartographers. 2012;**45**(1):10-18

[35] Datta S, Sarkar P, Das S, Sreshtha S, Lade P, Majumder S. How many eyeballs does a bug need? An empirical validation of Linus' law. In: International Conference on Agile Software Development. Cham: Springer; 2014. pp. 242-250

[36] Grinberg N, Joseph K, Friedland L, Swire-Thompson B, Lazer D. Fake news on twitter during the 2016 US presidential election. Science. 2019;**363**(6425):374-378

[37] Bovet A, Makse HA. Influence of fake news in twitter during the 2016 US presidential election. Nature Communications. 2019;**10**(1):1-14

[38] Elwood S, Goodchild MF, Sui D. Prospects for VGI research and the emerging fourth paradigm. In: Crowdsourcing Geographic Knowledge. Dordrecht: Springer; 2013. pp. 361-375

[39] de Bruijn JA, de Moel H, Jongman B, de Ruiter MC, Wagemaker J, Aerts JC. A global database of historic and real-time flood events based on social media. Scientific Data. 2019;**6**(1):1-12

[40] Shaluf IM. Technological disaster stages and management. Disaster Prevention and Management: An International Journal. 2008;**17**(1):114-126

[41] O'Brien S. Translation technology and disaster management. In: Minako O, editor. The Routledge Handbook of Translation and Technology. Abingdonon-Thames, UK: Routledge, Taylor and Francis Group; 2019

[42] Yin J, Lampert A, Cameron M, Robinson B, Power R. Using social media to enhance emergency situation awareness. IEEE Computer Society. 2012;**27**(6):52-59

[43] Smith L, Liang Q, James P, Lin W. Assessing the utility of social media as a data source for flood risk management using a real-time modelling framework. Journal of Flood Risk Management. 2017;**10**(3):370-380

[44] Wahlström M. New Sendai framework strengthens focus on reducing disaster risk. International Journal of Disaster Risk Science. 2015;**6**(2):200-201

[45] Whittaker J, McLennan B, Handmer J. A review of informal volunteerism in emergencies and disasters: Definition, opportunities and challenges. International Journal of Disaster Risk Reduction. 2015;**13**:358-368

[46] Arapostathis SG. Tweeting about floods of Messinia (Greece, September 2016)-towards a credible methodology for disaster management purposes. In: International Conference on Information Technology in Disaster Risk Reduction. Cham: Springer; 2018. pp. 142-154

[47] McDougall K. Using volunteered geographic information to map the Queensland floods. In: Proceedings of the Surveying & Spatial Sciences Biennial Conference. Wellington, New Zealand: Scion; 2011. pp. 21-25

[48] Dashti S, Palen L, Heris MP, Anderson KM, Anderson S, Anderson TJ. Supporting disaster reconnaissance with social media data: A design-oriented case study of the 2013 Colorado floods. In: Proceedings of the 11th International ISCRAM Conference. Pennsylvania, USA: University Park; 2013

[49] Arapostathis SG, Karantzia M. Mapping Information of Fire Events, from VGI Source (Twitter), for Effective Disaster Management (in Greece); the Fire of North-East Attica, August 2017, (Greece) Case Study. In: Advances in Remote Sensing and Geo Informatics Applications. Cham: Springer; 2019. pp. 257-260

[50] Ostermann FO, Spinsanti L. A conceptual workflow for automatically assessing the quality of volunteered geographic information for crisis management. In: Stan G, Wolfgang R, Fred T, editors. Proceedings of AGILE. Vol. 2011. Springer-Verlag; 2011. pp. 1-6

[51] Gao H, Barbier G, Goolsby R. Harnessing the crowdsourcing power of social media for disaster relief. IEEE Intelligent Systems. 2011;**26**(3):10-14

[52] Poser K, Dransch D. Volunteered geographic information for disaster management with application to rapid flood damage estimation. Geomatica. 2010;**64**(1):89-98

[53] Feng Y, Sester M. Extraction of pluvial flood relevant volunteered geographic information (VGI) by deep learning from user generated texts and photos. ISPRS International Journal of Geo-Information. 2018;**7**(2):39

[54] Arapostathis SG, Spyrou N, Drakatos G, Kalabokidis K, Lekkas E, Xanthopoulos G. Mapping information related to floods, extracted from VGI sources, for effective disaster management within the Greek Territory; the Floods of West Attica (November 2017 Greece) case study. In: Photis Y, editor. 11th International Conference of the Hellenic Geographical Society. Athens, Greece: Govostis Publishers; 2018

[55] Heltberg R, Bennett Siegel P, Jorgensen SL. Addressing human vulnerability to climate change: Toward a no-regrets approach. In: Photis Y, editor. Global Environmental Change. Athens, Greece: Govostis Publishers; 2009;**19**(1):89-99

[56] Leidig M, Teeuw R. Free software: A review, in the context of disaster management. International Journal of Applied Earth Observation and Geoinformation. 2015;**42**:49-56

[57] Ceccato L, Giannini V, Giupponi C. Participatory assessment of adaptation strategies to flood risk in the upper Brahmaputra and Danube river basins. Environmental Science & Policy. 2011;**14**(8):1163-1174

[58] Stefanakis. Technologies of Publishing Mapping Content in the World Wide Web (In Greek). Athens, Greece: New Technologies Publications; 2009

[59] Hirata E, Giannotti MA, Larocca APC, Quintanilha JA. Flooding and inundation collaborative mapping–use of the Crowdmap/Ushahidi platform in the city of Sao Paulo, Brazil. Journal of Flood Risk Management. 2018;**11**:S98-S109

[60] De Albuquerque JP, Herfort B, Brenning A, Zipf A. A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management. International Journal of Geographical Information Science. 2015;**29**(4):667-689

[61] Grunder-Fahrer S, Schlaf A, Wustmann S. How social media text analysis can inform disaster management GSCL 2017. LNAI. 2018;**10713**:199-207. DOI: 10.1007/978-3-319-73706-5\_17

**173**

*Fundamentals of Volunteered Geographic Information in Disaster Management Related to Floods*

information nuggets from disasterrelated messages in social media. In:

estimation of wildfire parameters from curated crowdsourcing. Scientific Reports. Department of Planning & Regional Development School of Engineering University of Thessaly - Greece; 2016. DOI: 10.1038/srep24206

[70] Resch B, Usländer F, Havas C. Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment. Cartography and Geographic Information Science.

2018;**45**(4):362-376

[69] Zhong X, Duckham M, Chong D, Tolhurst K. Real time

ISCRAM; 2013

*DOI: http://dx.doi.org/10.5772/intechopen.92225*

streaming api with Twitter's firehose. In: Seventh International AAAI Conference on Weblogs and Social Media; 2013

[62] Morstatter F, Pfeffer J, Liu H, Carley KM. Is the sample good enough?

Comparing data from Twitter's

[63] Iacus S. M., Porro G., Salini S., Siletti E. How to exploit Big Data from social networks: A subjective well-being indicator via Twitter. In: SIS Conference 28 of June 2017, Firenze, Italy; 2017

[64] Arapostathis SG, Isaak P,

[66] De Bruijn JA, de Moel H,

Journal of Geovisualization and Spatial Analysis. 2017. DOI: 10.1007/

[67] Arapostathis SG. Automated

[68] Imran M, Elbassuoni S,

Castillo C, Diaz F, Meier, P. Extracting

methods for effective geo-referencing of tweets related to disaster management. In: Perrakis K, editor. Laboratory of Computer Science Applications in Spatial Planning. International Geomatics Applications Conference – Book of Abstracts (Online). Greece: Department of Planning & Regional Development School of Engineering University of Thessaly; 2018. p. 62

s41651-017-0010-6

Jongman B, Wagemaker J, Aerts JCJH. TAGGS: Grouping tweets to improve global geoparsing for disaster response.

2016;**10**:839-852

Emmanuel S, George D, Ioannis K. A method for developing seismic intensity maps from twitter data. Journal of Civil Engineering and Architecture.

[65] Lee S, Farag M, Kanan T, Fox EA. Read between the lines: A machine learning approach for disambiguating the geo-location of tweets. In: Bogen PL, Allard SL, Mercer H, Beck M, editors. Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries. General Chairs. New York, NY, United States: Association for Computing Machinery; 2015. pp. 273-274

*Fundamentals of Volunteered Geographic Information in Disaster Management Related to Floods DOI: http://dx.doi.org/10.5772/intechopen.92225*

[62] Morstatter F, Pfeffer J, Liu H, Carley KM. Is the sample good enough? Comparing data from Twitter's streaming api with Twitter's firehose. In: Seventh International AAAI Conference on Weblogs and Social Media; 2013

*Flood Impact Mitigation and Resilience Enhancement*

Society. Athens, Greece: Govostis

[55] Heltberg R, Bennett Siegel P, Jorgensen SL. Addressing human vulnerability to climate change: Toward a no-regrets approach. In: Photis Y, editor. Global Environmental Change. Athens, Greece: Govostis Publishers;

[56] Leidig M, Teeuw R. Free software: A review, in the context of disaster management. International Journal of Applied Earth Observation and Geoinformation. 2015;**42**:49-56

[57] Ceccato L, Giannini V, Giupponi C. Participatory assessment of adaptation strategies to flood risk in the upper Brahmaputra and Danube river basins. Environmental Science & Policy.

[59] Hirata E, Giannotti MA, Larocca APC, Quintanilha JA. Flooding and inundation

Publishers; 2018

2009;**19**(1):89-99

2011;**14**(8):1163-1174

2009

[58] Stefanakis. Technologies of Publishing Mapping Content in the World Wide Web (In Greek). Athens, Greece: New Technologies Publications;

collaborative mapping–use of the Crowdmap/Ushahidi platform in the city of Sao Paulo, Brazil. Journal of Flood Risk

Management. 2018;**11**:S98-S109

Science. 2015;**29**(4):667-689

[61] Grunder-Fahrer S, Schlaf A, Wustmann S. How social media text analysis can inform disaster management GSCL 2017. LNAI. 2018;**10713**:199-207. DOI: 10.1007/978-3-319-73706-5\_17

[60] De Albuquerque JP, Herfort B, Brenning A, Zipf A. A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management. International Journal of Geographical Information

reconnaissance with social media data: A design-oriented case study of the 2013 Colorado floods. In: Proceedings of the 11th International ISCRAM Conference. Pennsylvania, USA: University Park;

[49] Arapostathis SG, Karantzia M. Mapping Information of Fire Events, from VGI Source (Twitter), for Effective Disaster Management (in Greece); the Fire of North-East Attica, August 2017, (Greece) Case Study. In: Advances in Remote Sensing and Geo Informatics Applications. Cham:

Springer; 2019. pp. 257-260

[50] Ostermann FO, Spinsanti L. A conceptual workflow for automatically assessing the quality of volunteered geographic information for crisis management. In: Stan G, Wolfgang R, Fred T, editors. Proceedings of AGILE. Vol. 2011. Springer-Verlag; 2011. pp. 1-6

[51] Gao H, Barbier G, Goolsby R. Harnessing the crowdsourcing power of social media for disaster relief. IEEE Intelligent Systems. 2011;**26**(3):10-14

[52] Poser K, Dransch D. Volunteered geographic information for disaster management with application to rapid flood damage estimation. Geomatica.

[53] Feng Y, Sester M. Extraction of pluvial flood relevant volunteered geographic information (VGI) by deep learning from user generated texts and photos. ISPRS International Journal of

Geo-Information. 2018;**7**(2):39

[54] Arapostathis SG, Spyrou N, Drakatos G, Kalabokidis K,

from VGI sources, for effective

Lekkas E, Xanthopoulos G. Mapping information related to floods, extracted

disaster management within the Greek Territory; the Floods of West Attica (November 2017 Greece) case study. In: Photis Y, editor. 11th International Conference of the Hellenic Geographical

2010;**64**(1):89-98

2013

**172**

[63] Iacus S. M., Porro G., Salini S., Siletti E. How to exploit Big Data from social networks: A subjective well-being indicator via Twitter. In: SIS Conference 28 of June 2017, Firenze, Italy; 2017

[64] Arapostathis SG, Isaak P, Emmanuel S, George D, Ioannis K. A method for developing seismic intensity maps from twitter data. Journal of Civil Engineering and Architecture. 2016;**10**:839-852

[65] Lee S, Farag M, Kanan T, Fox EA. Read between the lines: A machine learning approach for disambiguating the geo-location of tweets. In: Bogen PL, Allard SL, Mercer H, Beck M, editors. Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries. General Chairs. New York, NY, United States: Association for Computing Machinery; 2015. pp. 273-274

[66] De Bruijn JA, de Moel H, Jongman B, Wagemaker J, Aerts JCJH. TAGGS: Grouping tweets to improve global geoparsing for disaster response. Journal of Geovisualization and Spatial Analysis. 2017. DOI: 10.1007/ s41651-017-0010-6

[67] Arapostathis SG. Automated methods for effective geo-referencing of tweets related to disaster management. In: Perrakis K, editor. Laboratory of Computer Science Applications in Spatial Planning. International Geomatics Applications Conference – Book of Abstracts (Online). Greece: Department of Planning & Regional Development School of Engineering University of Thessaly; 2018. p. 62

[68] Imran M, Elbassuoni S, Castillo C, Diaz F, Meier, P. Extracting information nuggets from disasterrelated messages in social media. In: ISCRAM; 2013

[69] Zhong X, Duckham M, Chong D, Tolhurst K. Real time estimation of wildfire parameters from curated crowdsourcing. Scientific Reports. Department of Planning & Regional Development School of Engineering University of Thessaly - Greece; 2016. DOI: 10.1038/srep24206

[70] Resch B, Usländer F, Havas C. Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment. Cartography and Geographic Information Science. 2018;**45**(4):362-376

**Chapter 10**

**Abstract**

Impact and Mitigation Strategies

*Zahiraniza Mustaffa, Ebrahim Hamid Hussein Al-Qadami,*

This chapter presents a flood risk management system for vehicles at roadways, developed from extensive experimental and numerical studies on the impact of flash floods towards vehicle instabilities. The system, easily addressed as FLO-LOW, developed to contradict the assumptions that a vehicle would be able to protect the passengers from the flood impact. Herein the hydrodynamics of flows moving across these roads coupled with the conditions of a static car that would result in vehicle instabilities has been studied. In an attempt to prevent fatalities in commonly flooded areas, permanent structures are installed to warn users regarding water depth at the flooded areas. The existing flood monitoring system only focuses on water conditions in rivers or lake in order to determine risks associated with floods. Thus, there is a need for a better system to understand and quantify a mechanism to determine hydrodynamics instability of a vehicle in floodwaters. FLO-LOW enables the road users to input their vehicle information for a proper estimation of safety limits upon crossing the flood prone area. Preferably, the system enables road users to describe and quantify parameters that might cause their vehicles to become vulnerable to being washed away as they enter the flooded area.

**Keywords:** vehicle hydrodynamics, instability modes, static vehicle, flooded roads,

Recently, flood occurrence possibilities have been increased globally due to two

main reasons namely, lands urbanization and climate changes [1, 2]. Climate changes caused by global worming increase the precipitation intensity and rapid lands urbanization leads to increase the flow run off process throughout the paved areas which becomes flooded during heavy rainfall events [3]. Floods can be categorized into three main types namely, riverine, coastal and flash floods [4]. However, Malaysian drainage and irrigation department (DID) classified floods into two main types namely, monsoon and flash flood [5]. Flash floods are considered the most dangerous when compared to others due to its high velocity and short time

warning which causes high mortality among people [6, 7].

for Flash Floods Occurrence

towards Vehicle Instabilities

*Syed Muzzamil Hussain Shah*

flood risk assessment system

**1. Introduction**

**175**

*and Khamaruzaman Wan Yusof*

## **Chapter 10**

## Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities

*Zahiraniza Mustaffa, Ebrahim Hamid Hussein Al-Qadami, Syed Muzzamil Hussain Shah and Khamaruzaman Wan Yusof*

## **Abstract**

This chapter presents a flood risk management system for vehicles at roadways, developed from extensive experimental and numerical studies on the impact of flash floods towards vehicle instabilities. The system, easily addressed as FLO-LOW, developed to contradict the assumptions that a vehicle would be able to protect the passengers from the flood impact. Herein the hydrodynamics of flows moving across these roads coupled with the conditions of a static car that would result in vehicle instabilities has been studied. In an attempt to prevent fatalities in commonly flooded areas, permanent structures are installed to warn users regarding water depth at the flooded areas. The existing flood monitoring system only focuses on water conditions in rivers or lake in order to determine risks associated with floods. Thus, there is a need for a better system to understand and quantify a mechanism to determine hydrodynamics instability of a vehicle in floodwaters. FLO-LOW enables the road users to input their vehicle information for a proper estimation of safety limits upon crossing the flood prone area. Preferably, the system enables road users to describe and quantify parameters that might cause their vehicles to become vulnerable to being washed away as they enter the flooded area.

**Keywords:** vehicle hydrodynamics, instability modes, static vehicle, flooded roads, flood risk assessment system

#### **1. Introduction**

Recently, flood occurrence possibilities have been increased globally due to two main reasons namely, lands urbanization and climate changes [1, 2]. Climate changes caused by global worming increase the precipitation intensity and rapid lands urbanization leads to increase the flow run off process throughout the paved areas which becomes flooded during heavy rainfall events [3]. Floods can be categorized into three main types namely, riverine, coastal and flash floods [4]. However, Malaysian drainage and irrigation department (DID) classified floods into two main types namely, monsoon and flash flood [5]. Flash floods are considered the most dangerous when compared to others due to its high velocity and short time warning which causes high mortality among people [6, 7].

Floods usually sweep light and non-stable objects through their ways; vehicles are among these objects which can be swept away at certain flow velocity and depth [8]. There are three forms of vehicles failure mode during floods including sliding, floating and toppling. Sliding occurs at high flow velocities and low water depths, floating occurs at high depths and toppling which basically occurs when the vehicle slides beyond the road edge [8]. Once moved, it can be easily following the flood path and cause damages to properties and road structures. Previous studies showed that around 50% of the total deaths during flash floods occur to the people inside vehicles [9]. Recently, many vehicles swept away during Sant Lorenc des Cardassar flash flood in the Spanish Balearic island of Majorca. A total of 10 persons were killed during this event 6 of them were inside their cars [10].

In the past, research on vehicle instabilities have been solely dedicated to stationary vehicles which are normally translated as vehicles parked on a road surface. A vehicle exposed to floodwater gets influenced by different hydrodynamic forces and prone to various instability modes. Outcomes on such modes are somehow recognized in the work on stationary vehicles, but, the existing approaches possess a limited ability to communicate with road user with respect to complicated hydrodynamic and nature of flooding. Thus, there is a need for a better system and method to understand and quantify a mechanism to determine hydrodynamics instability of a vehicle in floodwaters. Herein the flood risk assessment system called "FLO-LOW" has been introduced that enables the road users to input their vehicle information for a proper estimation of safety limits upon crossing the flood prone area. Preferably, the system enables road users to describe and quantify parameters that might cause their vehicles to become vulnerable to being washed

*Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities*

The procedures of estimating hydrodynamic instability values of a static vehicle

dominancy of additional forces through different combination of vehicle and water conditions. With that regards, this section covers the basic understanding on the impacts of hydrodynamic forces on a static vehicle. Further, the instability modes

Flooded objects are influenced by several hydrodynamic forces exerted by the incoming flow in different directions. Herein the impact of hydrodynamic forces on a static flooded vehicle have been discussed. In fact, there are several forces acting on a vehicle inside floodwaters which involves, drag (*FD*), buoyancy (*FB*), lift (*FL*), friction (*FR*), and gravitational (*FG*) forces as shown in **Figure 1**. Understanding the hydrodynamic forces acting on the vehicle body is important to understand the

Drag force (*FD*) is the flow pressure acting on one or more sides of the flooded vehicle. The pressure magnitude is controlled by different parameters including, flow velocity magnitude, vehicle direction, flow depth, affected area and flow density. The drag force is considered as the main force leading to the sliding

based on vehicle and flow condition information include determining the

based on the dominancy of hydrodynamic forces are further addressed.

away as they enter the flooded area.

*DOI: http://dx.doi.org/10.5772/intechopen.92731*

**2.1 Hydrodynamic forces**

instability modes.

*2.1.1 Drag force*

**Figure 1.**

**177**

*Hydrodynamic forces on a static flooded passenger car model [28].*

**2. Theory**

Vehicles are either parked or moving on a roadway. During a flooding condition, a parked or moving vehicle expects different hydrodynamic forces which leads to three failure modes as discussed earlier. Low-lying roads are the most critical location for moving vehicles due to the existence of the highest water depth and flow velocity [11]. Many drivers judge the flooded low-lying roads by their naked eyes and intentionally drive through it believing that the flow intensity and water depth are low which will not affect their car. Unfortunately, this can be considered as the main reason of increasing deaths during flood events [12].

During 1960s several cases related to flooded vehicles were reported in Australia. In 1967 (Bonham and Hattersley) conducted the first experimental work to find out the vehicle stability limits during floods [11, 13]. Bonham and Hattersley carried out several lab tests on a scaled car model (1:25) of Ford Falcon which was the common passenger car at that time. The flow direction was perpendicular to the car's longitudinal side. Vertical and horizontal forces were measured under different flow velocities and water depths. Finally, a friction coefficient of 0.3 was proposed [13]. In 1973, Gordon and Stone conducted laboratory investigation on Morris Mini car model with scale ratio of 1:16 following the same procedure of Bonham and Hattersley. However, the orientation of the model was different, where the car front side faced the flow direction. Two cases were investigated namely, front tires and rare tires locked mode. The results showed that the stability limits (depth � velocity) ranged between 0.3 and 1.0 m2 /s. The front tires locked condition was more stable and safer due to high friction resistance caused by the vehicle engine placed above the front axle [14]. In 1993, Keller and Mitsch carried out an analytical study to investigate the limits of stability of four car models namely, Suzuki Swift, Ford Laser, Toyota Corolla and Ford LTD. Vehicle's instability suggested to occur when the drag force was equal or greater than the friction force between the tires and road surface [15]. This leads to the formulation of the threshold velocity formula which can be given as,

$$w = 2 \times \left(\frac{\mu F\_G}{\rho C\_D A\_D}\right)^{0.5} \tag{1}$$

where, *v* is the incipient velocity, *μ* is the coefficient of friction which was set to 0.3 as derived by Bonham and Hattersley, *FG* is the axle load in wet conditions, *ρ* is the density of water, *CD* is the drag coefficient and *AD* is the submerged area projected normal to the flow.

Between 1993 and 2010 no studies have been reported regarding vehicle stability limits in floodwater. Australian Rainfall and Runoff the guidelines produced during this period were based on the results attained from the work of Bonham and Hattersley, Gordon and Stone, and Keller and Mitsch [16]. Between 2010 and 2019 several studies [17–28] have been published regarding flooded vehicles stability. This was due to the major changes in the vehicle design mainly due to weight, ground clearance, and hydrodynamic shape of modern cars [29].

*Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities DOI: http://dx.doi.org/10.5772/intechopen.92731*

In the past, research on vehicle instabilities have been solely dedicated to stationary vehicles which are normally translated as vehicles parked on a road surface. A vehicle exposed to floodwater gets influenced by different hydrodynamic forces and prone to various instability modes. Outcomes on such modes are somehow recognized in the work on stationary vehicles, but, the existing approaches possess a limited ability to communicate with road user with respect to complicated hydrodynamic and nature of flooding. Thus, there is a need for a better system and method to understand and quantify a mechanism to determine hydrodynamics instability of a vehicle in floodwaters. Herein the flood risk assessment system called "FLO-LOW" has been introduced that enables the road users to input their vehicle information for a proper estimation of safety limits upon crossing the flood prone area. Preferably, the system enables road users to describe and quantify parameters that might cause their vehicles to become vulnerable to being washed away as they enter the flooded area.

#### **2. Theory**

Floods usually sweep light and non-stable objects through their ways; vehicles are among these objects which can be swept away at certain flow velocity and depth [8]. There are three forms of vehicles failure mode during floods including sliding, floating and toppling. Sliding occurs at high flow velocities and low water depths, floating occurs at high depths and toppling which basically occurs when the vehicle slides beyond the road edge [8]. Once moved, it can be easily following the flood path and cause damages to properties and road structures. Previous studies showed that around 50% of the total deaths during flash floods occur to the people inside vehicles [9]. Recently, many vehicles swept away during Sant Lorenc des Cardassar flash flood in the Spanish Balearic island of Majorca. A total of 10 persons were

Vehicles are either parked or moving on a roadway. During a flooding condition, a parked or moving vehicle expects different hydrodynamic forces which leads to three failure modes as discussed earlier. Low-lying roads are the most critical location for moving vehicles due to the existence of the highest water depth and flow velocity [11]. Many drivers judge the flooded low-lying roads by their naked eyes and intentionally drive through it believing that the flow intensity and water depth are low which will not affect their car. Unfortunately, this can be considered as the

During 1960s several cases related to flooded vehicles were reported in Australia. In 1967 (Bonham and Hattersley) conducted the first experimental work to find out the vehicle stability limits during floods [11, 13]. Bonham and Hattersley carried out several lab tests on a scaled car model (1:25) of Ford Falcon which was the common passenger car at that time. The flow direction was perpendicular to the car's longitudinal side. Vertical and horizontal forces were measured under different flow velocities and water depths. Finally, a friction coefficient of 0.3 was proposed [13]. In 1973, Gordon and Stone conducted laboratory investigation on Morris Mini car model with scale ratio of 1:16 following the same procedure of Bonham and Hattersley. However, the orientation of the model was different, where the car front side faced the flow direction. Two cases were investigated namely, front tires and rare tires locked mode. The results showed that the stability limits (depth � velocity) ranged between 0.3

/s. The front tires locked condition was more stable and safer due to high

friction resistance caused by the vehicle engine placed above the front axle [14]. In 1993, Keller and Mitsch carried out an analytical study to investigate the limits of stability of four car models namely, Suzuki Swift, Ford Laser, Toyota Corolla and Ford LTD. Vehicle's instability suggested to occur when the drag force was equal or greater than the friction force between the tires and road surface [15]. This leads to

the formulation of the threshold velocity formula which can be given as,

ground clearance, and hydrodynamic shape of modern cars [29].

*<sup>v</sup>* <sup>¼</sup> <sup>2</sup> � *<sup>μ</sup>FG*

*ρCDAD* <sup>0</sup>*:*<sup>5</sup>

where, *v* is the incipient velocity, *μ* is the coefficient of friction which was set to 0.3 as derived by Bonham and Hattersley, *FG* is the axle load in wet conditions, *ρ* is the density of water, *CD* is the drag coefficient and *AD* is the submerged area

Between 1993 and 2010 no studies have been reported regarding vehicle stability limits in floodwater. Australian Rainfall and Runoff the guidelines produced during this period were based on the results attained from the work of Bonham and Hattersley, Gordon and Stone, and Keller and Mitsch [16]. Between 2010 and 2019 several studies [17–28] have been published regarding flooded vehicles stability. This was due to the major changes in the vehicle design mainly due to weight,

(1)

killed during this event 6 of them were inside their cars [10].

*Flood Impact Mitigation and Resilience Enhancement*

main reason of increasing deaths during flood events [12].

and 1.0 m2

**176**

projected normal to the flow.

The procedures of estimating hydrodynamic instability values of a static vehicle based on vehicle and flow condition information include determining the dominancy of additional forces through different combination of vehicle and water conditions. With that regards, this section covers the basic understanding on the impacts of hydrodynamic forces on a static vehicle. Further, the instability modes based on the dominancy of hydrodynamic forces are further addressed.

#### **2.1 Hydrodynamic forces**

Flooded objects are influenced by several hydrodynamic forces exerted by the incoming flow in different directions. Herein the impact of hydrodynamic forces on a static flooded vehicle have been discussed. In fact, there are several forces acting on a vehicle inside floodwaters which involves, drag (*FD*), buoyancy (*FB*), lift (*FL*), friction (*FR*), and gravitational (*FG*) forces as shown in **Figure 1**. Understanding the hydrodynamic forces acting on the vehicle body is important to understand the instability modes.

#### *2.1.1 Drag force*

Drag force (*FD*) is the flow pressure acting on one or more sides of the flooded vehicle. The pressure magnitude is controlled by different parameters including, flow velocity magnitude, vehicle direction, flow depth, affected area and flow density. The drag force is considered as the main force leading to the sliding

**Figure 1.** *Hydrodynamic forces on a static flooded passenger car model [28].*

instability failure mode for static cars. The general drag force formula can be expressed as follow:

$$F\_D = \frac{1}{2} \rho C\_d A\_d v^2 \tag{2}$$

where, *ρ* is flow density, *Cd* is the drag coefficient, *Ad* is the affected area, *v* is flow velocity.

#### *2.1.2 Friction force*

Friction force (*FR*) can be defined as the reaction between the tires and ground surface against the drag force (*FD*). Friction resistance is the main force which keep the vehicle stable against sliding. The frictional resistance is influenced by the ground surface condition (rough/smooth), tires material and vehicle weight. The friction force can be written as follow:

$$F\_R = \mu F\_G \tag{3}$$

*2.1.5 Gravitational force*

*Instability failure modes of a flooded vehicle [28].*

*DOI: http://dx.doi.org/10.5772/intechopen.92731*

force and *FL* is the left force.

**2.2 Instability modes**

submerged).

the flood.

**179**

expressed as:

**Figure 2.**

The gravitational force (*FG*) is the vehicle effective weight and it can be

*Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities*

where, *Fg* is the curb weight of the vehicle at dry condition, *FB* is the buoyancy

The three modes of instability for stationary vehicles inside floodwaters are shown in **Figure 2**. In terms of hydrodynamic forces, when the drag force (*FD*) exerted by the flow exceeds the friction force (*FR*) between the tires and ground, sliding occurs. While floating takes place when the buoyancy (*FB*) and lift (*FL*) forces are equal to or more than the vehicle weight. During flood events vehicle instability is influenced by different parameters including, flow velocity, water depth, vehicle orientation against flow direction, vehicle characteristics (length, width, ground clearance, weight, tires condition, and hydrodynamic design), road

The critical situation usually occurs when the vehicle's longitudinal side is perpendicular to the flow direction. At this orientation the drag force reaches to the maximum value. Further, vehicles parked on a flat road surface are more stable when compared to the vehicles on slopes. Additionally, vehicle ground clearance and weight play main rule in terms of floating instability mode. Vehicles with higher ground clearance and weight are more stable. However, road roughness and tires condition (new/old) have significant effects on the stability limits. Higher road surface roughness gives higher friction reaction against the drag force exerted by

The present invention generally relates to a flood risk assessment system and methods, more particularly a flood risk assessment system and a method for determining hydrodynamic instability of a stationary vehicles in a flood-prone area.

slope, road roughness, and vehicle submergence level (fully or partially

**3. Flow dynamics of the vehicle linkage to flood**

**management – FLO-LOW**

*FG* ¼ *Fg* � ð Þ *FB* � *FL* (6)

where (*FG*) is the net normal reaction against the vehicle weight and *μ* is the friction coefficient. *μ* has different values based on the vehicle orientation and tire's axles locked conditions. *μ* Values ranged between 0.3 and 1.0, however the value of 0.3 has been considered conservative and effective for majority of the road condition and tire types.

#### *2.1.3 Buoyancy force*

The pressure exerted by the flow in the upward direction against the object weight is called buoyancy force (*FB*). In other words, it can be defined as fluid weight which is displaced by the immersed part of the object. In general, the main parameters effecting the buoyancy force are, object density, immersed volume and fluid density. Buoyancy force (*FB*) can be written as:

$$F\_B = \rho \mathbf{g} \mathbf{V} \tag{4}$$

where, *ρ* is fluid density, *g* is the gravity, and *V* is the object submerged volume. Flooded vehicles are mainly subjected to extreme *FB* at low flow velocity and high flow depths that mostly leads to floating instability mode.

#### *2.1.4 Lift force*

High velocity floodwaters flowing around the lower surface of the vehicle generates a force acting on the surface perpendicular to the flow direction. This force is called lift force (*FL*) which is affected by several parameters including, flow velocity, affected area and fluid density. The general expression of the lift force can be written as:

$$F\_L = \frac{1}{2} \rho C\_l A\_l v^2 \tag{5}$$

where, *ρ* is flow density, *CL* is the lift coefficient, *v* is the flow velocity, and *AL* is the affected area by the lift force (vehicle plane area).

*Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities DOI: http://dx.doi.org/10.5772/intechopen.92731*

**Figure 2.** *Instability failure modes of a flooded vehicle [28].*

#### *2.1.5 Gravitational force*

instability failure mode for static cars. The general drag force formula can be

*FD* <sup>¼</sup> <sup>1</sup> 2

where, *ρ* is flow density, *Cd* is the drag coefficient, *Ad* is the affected area, *v* is

Friction force (*FR*) can be defined as the reaction between the tires and ground surface against the drag force (*FD*). Friction resistance is the main force which keep the vehicle stable against sliding. The frictional resistance is influenced by the ground surface condition (rough/smooth), tires material and vehicle weight. The

where (*FG*) is the net normal reaction against the vehicle weight and *μ* is the friction coefficient. *μ* has different values based on the vehicle orientation and tire's axles locked conditions. *μ* Values ranged between 0.3 and 1.0, however the value of 0.3 has been considered conservative and effective for majority of the road condi-

The pressure exerted by the flow in the upward direction against the object weight is called buoyancy force (*FB*). In other words, it can be defined as fluid weight which is displaced by the immersed part of the object. In general, the main parameters effecting the buoyancy force are, object density, immersed volume and

where, *ρ* is fluid density, *g* is the gravity, and *V* is the object submerged volume. Flooded vehicles are mainly subjected to extreme *FB* at low flow velocity and high

High velocity floodwaters flowing around the lower surface of the vehicle generates a force acting on the surface perpendicular to the flow direction. This force is called lift force (*FL*) which is affected by several parameters including, flow velocity, affected area and fluid density. The general expression of the lift force can be

where, *ρ* is flow density, *CL* is the lift coefficient, *v* is the flow velocity, and *AL* is

*FL* <sup>¼</sup> <sup>1</sup> 2

*ρCdAdv*<sup>2</sup> (2)

*FR* ¼ *μFG* (3)

*FB* ¼ *ρgV* (4)

*ρClAlv*<sup>2</sup> (5)

expressed as follow:

flow velocity.

*2.1.2 Friction force*

tion and tire types.

*2.1.3 Buoyancy force*

*2.1.4 Lift force*

written as:

**178**

friction force can be written as follow:

*Flood Impact Mitigation and Resilience Enhancement*

fluid density. Buoyancy force (*FB*) can be written as:

flow depths that mostly leads to floating instability mode.

the affected area by the lift force (vehicle plane area).

The gravitational force (*FG*) is the vehicle effective weight and it can be expressed as:

$$F\_G = F\_\mathfrak{g} - (F\_B - F\_L) \tag{6}$$

where, *Fg* is the curb weight of the vehicle at dry condition, *FB* is the buoyancy force and *FL* is the left force.

#### **2.2 Instability modes**

The three modes of instability for stationary vehicles inside floodwaters are shown in **Figure 2**. In terms of hydrodynamic forces, when the drag force (*FD*) exerted by the flow exceeds the friction force (*FR*) between the tires and ground, sliding occurs. While floating takes place when the buoyancy (*FB*) and lift (*FL*) forces are equal to or more than the vehicle weight. During flood events vehicle instability is influenced by different parameters including, flow velocity, water depth, vehicle orientation against flow direction, vehicle characteristics (length, width, ground clearance, weight, tires condition, and hydrodynamic design), road slope, road roughness, and vehicle submergence level (fully or partially submerged).

The critical situation usually occurs when the vehicle's longitudinal side is perpendicular to the flow direction. At this orientation the drag force reaches to the maximum value. Further, vehicles parked on a flat road surface are more stable when compared to the vehicles on slopes. Additionally, vehicle ground clearance and weight play main rule in terms of floating instability mode. Vehicles with higher ground clearance and weight are more stable. However, road roughness and tires condition (new/old) have significant effects on the stability limits. Higher road surface roughness gives higher friction reaction against the drag force exerted by the flood.

#### **3. Flow dynamics of the vehicle linkage to flood management – FLO-LOW**

The present invention generally relates to a flood risk assessment system and methods, more particularly a flood risk assessment system and a method for determining hydrodynamic instability of a stationary vehicles in a flood-prone area.

### **3.1 The system**

The present invention, FLO-LOW relates to an online decision-making tool for road users to decide the likelihood on crossing the areas that are prone to flooding. A flood risk assessment system provides a real-time monitoring of flood condition at a flood-prone area near rivers, streams, water course or lakes for determining the hydrodynamic instability of vehicles upon crossing such roads as shown in **Figure 3**. Based on complex hydrodynamics parameters, namely water depth, D and flow velocity, v associated with different types of vehicle suggest a threshold value that would lead to the possibility of a non-stationary vehicle instability in flood-prone area. The flood risk assessment system enables the road users to input their vehicle information for a proper estimation of safety limits upon crossing the flood prone area. Preferably, the system enables road users to describe and quantify parameters that might cause their vehicles to become vulnerable to being washed away as they enter the flood-prone area. The parameters may include but not limited to vehicle type, vehicle volume, vehicle location and vehicle direction.

The flood risk assessment system includes an application program that is running on a personal mobile communication device of an individual user, such that the road user is able to input vehicle and water condition information as well as to receive flood risk related data message and warning. **Figure 4** illustrates the schematic diagram of the flood risk assessment system according to an embodiment of the present invention. Generally, the flood risk assessment system comprises a plurality of sensing devices and a flood risk analysis terminal. The flood risk analysis terminal further comprises an input/output (I/O) module, a flood database and a data processing engine. The data processing engine further includes a flood analysis module and a flood warning module.

#### **3.2 Algorithm**

**Figure 5** exhibits the result vehicle instability based on complex hydrodynamics parameters carried through experimental and theoretical assessments. It is observed and evident in **Figure 5**, that other than different combinations of water depth, D and flow velocity, v, vehicle information which includes vehicle type is another important parameter to estimate vehicle instability in flood.

In accordance with an embodiment of the present invention and referring to **Figure 6**, a method for determining hydrodynamic instability risks of a vehicle comprises the step of detecting water conditions including water depth and water velocity data by a plurality of sensing device. The technique for detecting water conditions may include implementing a plurality of sensors and at least one communication device. The communication device may include but not limited to a rain

*Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities*

*DOI: http://dx.doi.org/10.5772/intechopen.92731*

*Compilation of previous works and validation of current research.*

**Figure 5.**

**181**

**Figure 4.**

*Flood risk analysis terminal.*

**Figure 3.** *Flood level monitor for low-lying area (FLO-LOW).*

*Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities DOI: http://dx.doi.org/10.5772/intechopen.92731*

**3.1 The system**

*Flood Impact Mitigation and Resilience Enhancement*

The present invention, FLO-LOW relates to an online decision-making tool for road users to decide the likelihood on crossing the areas that are prone to flooding. A flood risk assessment system provides a real-time monitoring of flood condition at a flood-prone area near rivers, streams, water course or lakes for determining the hydrodynamic instability of vehicles upon crossing such roads as shown in **Figure 3**. Based on complex hydrodynamics parameters, namely water depth, D and flow velocity, v associated with different types of vehicle suggest a threshold value that would lead to the possibility of a non-stationary vehicle instability in flood-prone area. The flood risk assessment system enables the road users to input their vehicle information for a proper estimation of safety limits upon crossing the flood prone area. Preferably, the system enables road users to describe and quantify parameters that might cause their vehicles to become vulnerable to being washed away as they enter the flood-prone area. The parameters may include but not limited to vehicle

The flood risk assessment system includes an application program that is running on a personal mobile communication device of an individual user, such that the road user is able to input vehicle and water condition information as well as to receive flood risk related data message and warning. **Figure 4** illustrates the schematic diagram of the flood risk assessment system according to an embodiment of the present invention. Generally, the flood risk assessment system comprises a plurality of sensing devices and a flood risk analysis terminal. The flood risk analysis terminal further comprises an input/output (I/O) module, a flood database and a data processing engine. The data processing engine further includes a flood analysis

**Figure 5** exhibits the result vehicle instability based on complex hydrodynamics parameters carried through experimental and theoretical assessments. It is observed and evident in **Figure 5**, that other than different combinations of water depth, D and flow velocity, v, vehicle information which includes vehicle type is another

type, vehicle volume, vehicle location and vehicle direction.

important parameter to estimate vehicle instability in flood.

module and a flood warning module.

*Flood level monitor for low-lying area (FLO-LOW).*

**3.2 Algorithm**

**Figure 3.**

**180**

**Figure 5.**

*Compilation of previous works and validation of current research.*

In accordance with an embodiment of the present invention and referring to **Figure 6**, a method for determining hydrodynamic instability risks of a vehicle comprises the step of detecting water conditions including water depth and water velocity data by a plurality of sensing device. The technique for detecting water conditions may include implementing a plurality of sensors and at least one communication device. The communication device may include but not limited to a rain

#### *Flood Impact Mitigation and Resilience Enhancement*

**4.2 Results and discussion**

*DOI: http://dx.doi.org/10.5772/intechopen.92731*

*Experimental setup [30].*

**Figure 7.**

than the lift force [30].

**183**

A varying combination of flow velocities and water depths were tested to investigate the threshold of vehicle instability. It was noticed that a static vehicle could become unstable or start to slide at two conditions, namely high flow velocity and low water depth or vice versa under the partial submergence and sub-critical flow conditions. Further, it was assumed that the lift coefficient (CL), drag coefficient (CD) and friction coefficient (μ) were set to a constant value. The study was limited to the partial submergence only and the vehicle behavior under full submergence was not taken into consideration. While preforming the experimental runs, the impact of buoyancy force was found dominant when the water depth exceeded 0.042 m. On the other hand, the impact of lift force was theoretically estimated. The assessment of lift force involved the assessment of planform area, theoretically, whereas the value of lift coefficient was obtained from a numerical study performed on a similar city car under partial submergence. Since the shear of the flow was mild as the study was performed under the sub-critical flow conditions, therefore the impact of the lift force was found insignificant when compared to the buoyancy force. Among the horizontal pushing forces, namely friction and drag force, the friction force was assessed by considering the friction coefficient value of 0.3, whereas the net weight of the vehicle was obtained by deducting the buoyancy force when the vehicle weight. On the other hand, to assess the drag force, the drag coefficient was taken 1.1 or 1.15 depending on flood water depth with respect to the chassis height. Similarly, the submerged area projected normal to the flow was determined for every water depth. Lastly, the velocity of the flow determined through the use of velocity meter. The impact of hydrodynamic forces, namely buoyancy, lift, friction and drag forces at varying combination of floodwater depth

*Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities*

Referring to the lift force as highlighted in **Figure 9**, it can be seen that its impact varied between 0.030 N and 0.303 N, whereas for the similar conditions, the impact of buoyancy force was found to be between 2.379 N and 4.596 N. Based on this observation it can be stated that the impact of lift force was insignificant to support the vertical pushing force and so does the floating instability when the flow condition is sub-critical. Basically, water provides best medium to develop drag rather

It has been stated that floating instability occurs when the vertical pushing force that is composed of both buoyancy and lift forces exceeds the vehicle weight of the immersed object. Under the sub-critical flow conditions, the flow velocities were found to be moderate as the range of Froude number ranged between 0.308 and 0.91. Thus, it is assumed that the flow shear was low and therefore the impact of lift force was disregarded. On that justification, it could be stated that when the flow

and velocity are shown in **Figures 8**–**11** respectively [30].

**Figure 6.** *Process flowchart.*

fall meter, a flood level meter and a visual display. The information related to water conditions that is obtained by the plurality of sensing device is displayed on a visual display screen which is visible to the road user. The flood analysis module then receives vehicle and water conditions information provided by the road user.

## **4. Experimental investigation**

To assess the hydrodynamics of a static vehicle under partial submergence and sub-critical flow conditions, a modern vehicle Volkswagen Scirocco was used with the scale ratio of 1:24 ensuring the similarity laws. Prior to perform the experiments following conditions were considered, namely (i) the rear tires of the vehicle were restricted to move, (ii) sealing capacity of the car was taken into consideration and (iii) to reduce the inconsistency in the data, the vehicle was placed at the same domain with different orientation angles [30].

#### **4.1 Experimental setup**

Experimental investigation were performed in the hydraulic flume of Universiti Teknologi PETRONAS, Malaysia as shown in **Figure 7**. The instability failure modes, namely sliding and floating instability of the vehicle were assessed by adjusting the discharge in the flume. The average flow velocity and the water depth were then recorded using the point gauge and Nixon Streamflo 430. To reduce the human error while assessing the failure modes, a monitoring laser was used to profound observe the vehicle movement in any direction. Proper procedures to enable assessment of flood hazard related to vehicles have been developed based on the studies performed earlier. To ensure similar conditions to that of actual road, the surface roughness of the platform where the experiments were conducted was determined which was found to be 0.017. This value nearly matched to the coefficient roughness of asphalt pavement, which is stated to be 0.016 for rough texture [30].

*Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities DOI: http://dx.doi.org/10.5772/intechopen.92731*

**Figure 7.** *Experimental setup [30].*

fall meter, a flood level meter and a visual display. The information related to water conditions that is obtained by the plurality of sensing device is displayed on a visual display screen which is visible to the road user. The flood analysis module then receives vehicle and water conditions information provided by the road user.

To assess the hydrodynamics of a static vehicle under partial submergence and sub-critical flow conditions, a modern vehicle Volkswagen Scirocco was used with the scale ratio of 1:24 ensuring the similarity laws. Prior to perform the experiments following conditions were considered, namely (i) the rear tires of the vehicle were restricted to move, (ii) sealing capacity of the car was taken into consideration and (iii) to reduce the inconsistency in the data, the vehicle was placed at the same

Experimental investigation were performed in the hydraulic flume of Universiti

Teknologi PETRONAS, Malaysia as shown in **Figure 7**. The instability failure modes, namely sliding and floating instability of the vehicle were assessed by adjusting the discharge in the flume. The average flow velocity and the water depth were then recorded using the point gauge and Nixon Streamflo 430. To reduce the human error while assessing the failure modes, a monitoring laser was used to profound observe the vehicle movement in any direction. Proper procedures to enable assessment of flood hazard related to vehicles have been developed based on the studies performed earlier. To ensure similar conditions to that of actual road, the surface roughness of the platform where the experiments were conducted was determined which was found to be 0.017. This value nearly matched to the coefficient roughness of asphalt pavement, which is stated to be

**4. Experimental investigation**

**Figure 6.** *Process flowchart.*

**4.1 Experimental setup**

0.016 for rough texture [30].

**182**

domain with different orientation angles [30].

*Flood Impact Mitigation and Resilience Enhancement*

#### **4.2 Results and discussion**

A varying combination of flow velocities and water depths were tested to investigate the threshold of vehicle instability. It was noticed that a static vehicle could become unstable or start to slide at two conditions, namely high flow velocity and low water depth or vice versa under the partial submergence and sub-critical flow conditions. Further, it was assumed that the lift coefficient (CL), drag coefficient (CD) and friction coefficient (μ) were set to a constant value. The study was limited to the partial submergence only and the vehicle behavior under full submergence was not taken into consideration. While preforming the experimental runs, the impact of buoyancy force was found dominant when the water depth exceeded 0.042 m. On the other hand, the impact of lift force was theoretically estimated. The assessment of lift force involved the assessment of planform area, theoretically, whereas the value of lift coefficient was obtained from a numerical study performed on a similar city car under partial submergence. Since the shear of the flow was mild as the study was performed under the sub-critical flow conditions, therefore the impact of the lift force was found insignificant when compared to the buoyancy force. Among the horizontal pushing forces, namely friction and drag force, the friction force was assessed by considering the friction coefficient value of 0.3, whereas the net weight of the vehicle was obtained by deducting the buoyancy force when the vehicle weight. On the other hand, to assess the drag force, the drag coefficient was taken 1.1 or 1.15 depending on flood water depth with respect to the chassis height. Similarly, the submerged area projected normal to the flow was determined for every water depth. Lastly, the velocity of the flow determined through the use of velocity meter. The impact of hydrodynamic forces, namely buoyancy, lift, friction and drag forces at varying combination of floodwater depth and velocity are shown in **Figures 8**–**11** respectively [30].

Referring to the lift force as highlighted in **Figure 9**, it can be seen that its impact varied between 0.030 N and 0.303 N, whereas for the similar conditions, the impact of buoyancy force was found to be between 2.379 N and 4.596 N. Based on this observation it can be stated that the impact of lift force was insignificant to support the vertical pushing force and so does the floating instability when the flow condition is sub-critical. Basically, water provides best medium to develop drag rather than the lift force [30].

It has been stated that floating instability occurs when the vertical pushing force that is composed of both buoyancy and lift forces exceeds the vehicle weight of the immersed object. Under the sub-critical flow conditions, the flow velocities were found to be moderate as the range of Froude number ranged between 0.308 and 0.91. Thus, it is assumed that the flow shear was low and therefore the impact of lift force was disregarded. On that justification, it could be stated that when the flow

conditions are sub-critical, floating instability occurs only when the buoyancy force exceed the vehicle weight. On the other hand, sliding instability was validated based on the condition, i.e., *FD* more than *FR*. The vehicle was found stable when the frictional force was greater than the drag force [30].

**5. Numerical investigation**

*Influence of drag force on the model vehicle [30].*

*DOI: http://dx.doi.org/10.5772/intechopen.92731*

follows:

**Figure 11.**

**5.1 Computational fluid dynamic model**

*VF ∂ρ ∂t* þ *∂*

*uAx ∂w ∂x*

*uAx ∂u ∂x*

*uAx ∂v ∂x*

accelerations in the coordinate direction (*x*, *y*, *z*).

*∂u ∂t* þ 1 *VF*

*∂v ∂t* þ 1 *VF*

*∂w ∂t* þ 1 *VF*

**185**

*<sup>∂</sup><sup>x</sup>* ð Þþ *uAx*

þ *vAy*

þ *vAy*

<sup>þ</sup> *vAyR <sup>∂</sup><sup>w</sup> ∂y*

*∂ ∂y vAy* <sup>þ</sup>

*∂u ∂y*

*∂v ∂y*

þ *wAz*

þ *wAz*

þ *wAz*

where VF is the fraction of open volume, *ρ* is the density, (*v*, *u*, *w*) and (*Ax*, *Ay*, *Az*) are the velocity and the fractional areas components in the (*x*, *y*, *z*) directions, *RSOR* is the source term of density, *p* is the pressure, (*Gx*, *Gy*, *Gz*) are the body accelarations in the coordinate direction (*x*, *y*, *z*), and ( *f <sup>x</sup>*, *f <sup>y</sup>*, *f <sup>z</sup>*) are viscous

*∂ ∂z*

> ¼ � <sup>1</sup> *ρ ∂p ∂x*

¼ � <sup>1</sup> *ρ ∂p ∂y*

¼ � <sup>1</sup> *ρ ∂p ∂z*

*∂u ∂z*

*∂v ∂z*

*∂w ∂z*

ð Þ¼ *wAz R*SOR (7)

þ *Gx* þ *f <sup>x</sup>* (8)

þ *Gy* þ *f <sup>y</sup>* (9)

þ *Gz* þ *f <sup>z</sup>* (10)

Numerical investigation of the flooded vehicles could assist to evaluate the instability failure modes for both scaled down and prototype models. At the same time the results and measured forces are more detailed. In this section, numerical simulation of a stationary flooded passenger car model have been discussed.

*Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities*

In this study, FLOW-3D software was used for numerical simulation purpose. FLOW-3D uses an orthogonal mesh defined in terms of either Cartesian or cylindrical coordinates. Three different types of mesh can be used (uniform meshes, non-uniform mesh, and multi-block mesh). In this case uniform single block mesh was used. However, mass continuity and momentum equations were solved to simulate 3D fluid flow. These equations can be written for incompressible flow as

**Figure 8.** *Influence of buoyancy on the model vehicle [30].*

**Figure 9.**

*Influence of lift force on the model vehicle [30].*

**Figure 10.** *Influence of friction force on model vehicle [30].*

*Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities DOI: http://dx.doi.org/10.5772/intechopen.92731*

**Figure 11.** *Influence of drag force on the model vehicle [30].*

## **5. Numerical investigation**

conditions are sub-critical, floating instability occurs only when the buoyancy force exceed the vehicle weight. On the other hand, sliding instability was validated based on the condition, i.e., *FD* more than *FR*. The vehicle was found stable when the

frictional force was greater than the drag force [30].

*Flood Impact Mitigation and Resilience Enhancement*

**Figure 8.**

**Figure 9.**

**Figure 10.**

**184**

*Influence of buoyancy on the model vehicle [30].*

*Influence of lift force on the model vehicle [30].*

*Influence of friction force on model vehicle [30].*

Numerical investigation of the flooded vehicles could assist to evaluate the instability failure modes for both scaled down and prototype models. At the same time the results and measured forces are more detailed. In this section, numerical simulation of a stationary flooded passenger car model have been discussed.

#### **5.1 Computational fluid dynamic model**

In this study, FLOW-3D software was used for numerical simulation purpose. FLOW-3D uses an orthogonal mesh defined in terms of either Cartesian or cylindrical coordinates. Three different types of mesh can be used (uniform meshes, non-uniform mesh, and multi-block mesh). In this case uniform single block mesh was used. However, mass continuity and momentum equations were solved to simulate 3D fluid flow. These equations can be written for incompressible flow as follows:

$$V\_F \frac{\partial \rho}{\partial t} + \frac{\partial}{\partial x}(uA\_x) + \frac{\partial}{\partial y}(vA\_y) + \frac{\partial}{\partial z}(wA\_z) = R\_{\text{SOR}} \tag{7}$$

$$\frac{\partial u}{\partial t} + \frac{1}{V\_F} \left\{ u A\_\mathbf{x} \frac{\partial u}{\partial \mathbf{x}} + v A\_\mathbf{y} \frac{\partial u}{\partial \mathbf{y}} + w A\_\mathbf{z} \frac{\partial u}{\partial \mathbf{z}} \right\} = -\frac{1}{\rho} \frac{\partial p}{\partial \mathbf{x}} + G\_\mathbf{x} + f\_\mathbf{x} \tag{8}$$

$$\frac{\partial v}{\partial t} + \frac{1}{V\_F} \left\{ u A\_x \frac{\partial v}{\partial x} + v A\_y \frac{\partial v}{\partial y} + w A\_z \frac{\partial v}{\partial z} \right\} = -\frac{1}{\rho} \frac{\partial p}{\partial y} + G\_\mathcal{I} + f\_\mathcal{y} \tag{9}$$

$$\frac{\partial w}{\partial t} + \frac{1}{V\_F} \left\{ u A\_x \frac{\partial w}{\partial x} + v A\_y R \frac{\partial w}{\partial y} + w A\_z \frac{\partial w}{\partial z} \right\} = -\frac{1}{\rho} \frac{\partial p}{\partial z} + G\_x + f\_x \tag{10}$$

where VF is the fraction of open volume, *ρ* is the density, (*v*, *u*, *w*) and (*Ax*, *Ay*, *Az*) are the velocity and the fractional areas components in the (*x*, *y*, *z*) directions, *RSOR* is the source term of density, *p* is the pressure, (*Gx*, *Gy*, *Gz*) are the body accelarations in the coordinate direction (*x*, *y*, *z*), and ( *f <sup>x</sup>*, *f <sup>y</sup>*, *f <sup>z</sup>*) are viscous accelerations in the coordinate direction (*x*, *y*, *z*).

### **5.2 Methodology**

FLOW-3D is a commercial code which uses finite volume method (FVM) and turbulence models to solve continuity and Navier stokes equations [31, 32]. The FLOW-3D software also allow the numerical simulation under six degree of freedom which can represent the real experiment condition [28]. To study vehicles stability limits, small passenger car (Perodua Viva) was used to investigate the floating and sliding instability modes under different flow conditions. Two different setup modes were constructed for both floating and sliding as follows.

#### *5.2.1 Floating condition*

Car model was tested in two scale ratios (prototype and 1:10) to find out the difference in terms of hydrodynamic forces as well as find out the floating depth and buoyancy force. The stability limits were investigated under sub-critical flow condition, this was because of that the main force causing floating failure mode is the buoyancy force which exerted by the flow depth. The car models were placed inside close boxes with dimensions of (900 cm � 500 cm � 220 cm, for prototype) and (90 cm � 50 cm � 22 cm for sealed down model-1:10) then the waters flowed into the box gradually. **Figure 12** shows the setup and boundary conditions for both cases. The car model was tested under six degree of freedom, where the model movement in all directions can be noticed and measured. One history probe was placed beside the car model to measure the hydraulic parameters (flow velocity, Froude's number, and water depth). Car models were defined as coupled motion object, this definition allow the code to calculate the hydrodynamic forces exerted by the flow on the models outer surfaces in all directions (X, Y, and Z).

number. Four combinations of flow velocities and water depths were simulated as

*Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities*

In terms of floating instability mode, both scale ratios showed the same pattern

and values (floating depths and buoyancy forces). **Figures 14** and **15** show the relationship between the model center of mass (COM) and the buoyancy forces for scaled down and prototype models respectively. For scaled down model, the buoyancy force at fully floating condition was 8.9797 N which can be scaled up to 8979 N. While it was 9155 N for prototype model. When compared, the buoyancy forces numerically simulated and the value from Eq. (4) the differences were 1.94

and 0.21% for scaled and prototype models respectively. In addition, form **Figures 14** and **15** it is clear that the high and sudden changes in models COM

**Water depth (***cm***) Flow velocity (***cm=s***) <sup>D</sup>**�*<sup>v</sup>* **(***m***<sup>2</sup>***=s***) Model condition** 0.14 0.89 0.12 Stable 0.29 0.66 0.19 Stable 0.16 1.70 0.27 Stable 0.12 2.88 0.35 Unstable

occurred once bouncy force exceeded the model weight.

*Water depths and flow velocities combination to assess sliding instability.*

*Relationship between the buoyancy force and car center of mass with time (1:10).*

shown in **Table 1**.

**Figure 13.**

**Table 1.**

**Figure 14.**

**187**

**5.3 Results and discussion**

*Numerical setup and boundary conditions for sliding test.*

*DOI: http://dx.doi.org/10.5772/intechopen.92731*

#### *5.2.2 Sliding condition*

To investigate sliding instability limits under different flow conditions, the car model with scaled ratio of 1:10 was placed inside a flume with dimensions of (300 cm � 90 cm � 22 cm), and the longitudinal side faced the flow direction. This orientation was selected because it was considered as the most critical case compared to th other orientations [29]. The numerical runs were conducted under 6 degree of freedoms condition, and coupled motion definition between flow and car model was selected. The friction coefficient was set to 0.52 based on the previous experimental tests [11]. **Figure 13** shows the numerical setups and the boundary condition definitions. One history probe was placed in front of the car model with a distance equal to the car length to measure flow velocity, water depth, and Froude's

**Figure 12.** *Numerical simulation setup for floating testing (a) 1:10 and (b) prototype.*

*Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities DOI: http://dx.doi.org/10.5772/intechopen.92731*

**Figure 13.** *Numerical setup and boundary conditions for sliding test.*

number. Four combinations of flow velocities and water depths were simulated as shown in **Table 1**.

#### **5.3 Results and discussion**

**5.2 Methodology**

*Flood Impact Mitigation and Resilience Enhancement*

*5.2.1 Floating condition*

*5.2.2 Sliding condition*

**Figure 12.**

**186**

FLOW-3D is a commercial code which uses finite volume method (FVM) and turbulence models to solve continuity and Navier stokes equations [31, 32]. The FLOW-3D software also allow the numerical simulation under six degree of freedom which can represent the real experiment condition [28]. To study vehicles stability limits, small passenger car (Perodua Viva) was used to investigate the floating and sliding instability modes under different flow conditions. Two differ-

Car model was tested in two scale ratios (prototype and 1:10) to find out the difference in terms of hydrodynamic forces as well as find out the floating depth and buoyancy force. The stability limits were investigated under sub-critical flow condition, this was because of that the main force causing floating failure mode is the buoyancy force which exerted by the flow depth. The car models were placed inside close boxes with dimensions of (900 cm � 500 cm � 220 cm, for prototype) and (90 cm � 50 cm � 22 cm for sealed down model-1:10) then the waters flowed into the box gradually. **Figure 12** shows the setup and boundary conditions for both cases. The car model was tested under six degree of freedom, where the model movement in all directions can be noticed and measured. One history probe was placed beside the car model to measure the hydraulic parameters (flow velocity, Froude's number, and water depth). Car models were defined as coupled motion object, this definition allow the code to calculate the hydrodynamic forces exerted

ent setup modes were constructed for both floating and sliding as follows.

by the flow on the models outer surfaces in all directions (X, Y, and Z).

To investigate sliding instability limits under different flow conditions, the car

model with scaled ratio of 1:10 was placed inside a flume with dimensions of (300 cm � 90 cm � 22 cm), and the longitudinal side faced the flow direction. This orientation was selected because it was considered as the most critical case compared to th other orientations [29]. The numerical runs were conducted under 6 degree of freedoms condition, and coupled motion definition between flow and car model was selected. The friction coefficient was set to 0.52 based on the previous experimental tests [11]. **Figure 13** shows the numerical setups and the boundary condition definitions. One history probe was placed in front of the car model with a distance equal to the car length to measure flow velocity, water depth, and Froude's

*Numerical simulation setup for floating testing (a) 1:10 and (b) prototype.*

In terms of floating instability mode, both scale ratios showed the same pattern and values (floating depths and buoyancy forces). **Figures 14** and **15** show the relationship between the model center of mass (COM) and the buoyancy forces for scaled down and prototype models respectively. For scaled down model, the buoyancy force at fully floating condition was 8.9797 N which can be scaled up to 8979 N. While it was 9155 N for prototype model. When compared, the buoyancy forces numerically simulated and the value from Eq. (4) the differences were 1.94 and 0.21% for scaled and prototype models respectively. In addition, form **Figures 14** and **15** it is clear that the high and sudden changes in models COM occurred once bouncy force exceeded the model weight.


**Table 1.**

*Water depths and flow velocities combination to assess sliding instability.*

**Figures 16** and **17** show the relationship between water depth and buoyancy force with respect to the time for scaled and prototype models, respectively. The buoyancy force increased gradually with the depth increment and the values of floating depths were 3.7 cm (which can be scaled up to 37 cm) and 37.5 cm for scaled and prototype models, respectively. From both simulated parameters (buoyancy force and floating depth) it can be concluded that the numerical modeling by using FLOW-3D software gave accurate results and allowed to test vehicles in real scale.

In terms of sliding, the results showed that the car model at the *D* � *v* value less

while the sliding condition occurred at supercritical flow condition. Car model remained at its original location in cases no. 1, 2, and 3 where the values of *D* � *v*

*Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities*

under different road slops as well as under different orientations.

*Numerical results validated with previous experimental study [33].*

*DOI: http://dx.doi.org/10.5772/intechopen.92731*

location and sliding failure mode was noticed. The numerical results were compared with Australiana rainfall and runoff guidelines (2011) [32] and good agreement was noticed as shown in **Figure 18**. From the results it can be concluded that, numerical simulation using FLOW-3D can give good predictions and results related to vehicle instability limits. Several car models can be tested numerically flowing same steps

In the past, research on vehicle instabilities have been solely dedicated to stationary vehicles which are normally translated as vehicles parked on a road surface. A vehicle exposed to floodwater gets influenced by different hydrodynamic forces and prone to various instability modes. Outcomes on such modes are somehow recognized in the work on stationary vehicles, but the existing approaches possess a limited ability to communicate with road user with respect to complicated hydrodynamic and nature of flooding. In an attempt to prevent fatalities in commonly flooded or flood-prone areas, permanent structures are installed to warn users regarding the depth of the water at the flooded area. The existing flood monitoring system only focuses on water conditions in rivers or lake in order to determine risks associated with floods. The present invention, FLO-LOW relates to an online decision-making tool for road users to decide the likelihood on crossing low-lying areas that are prone to flooding. The flood risk assessment system provides a realtime monitoring of flood condition at flood-prone area for determining the hydro-

FLO-LOW different individual component as well as the whole complete system have been submitted for Intellectual Property under three categories, namely Patent (PI 2017702574), Patent (PI 2019001397), Industrial Design (17-E0208-0101) and

/s was safe. At subcritical flow condition, the car tended to be float,

/s. In case no. 4 the car model dragged from its original

than of 0.35 m<sup>2</sup>

**Figure 18.**

**6. Conclusions**

dynamic instability of a vehicle.

**7. Recognition**

**189**

were less then 0.35 m<sup>2</sup>

**Figure 15.** *Relationship between the buoyancy force and car center of mass with time (prototype).*

**Figure 16.** *Relationship between the buoyancy force and water depth with time (1:10).*

**Figure 17.** *Relationship between the buoyancy force and water depth with time (prototype).*

*Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities DOI: http://dx.doi.org/10.5772/intechopen.92731*

**Figure 18.** *Numerical results validated with previous experimental study [33].*

In terms of sliding, the results showed that the car model at the *D* � *v* value less than of 0.35 m<sup>2</sup> /s was safe. At subcritical flow condition, the car tended to be float, while the sliding condition occurred at supercritical flow condition. Car model remained at its original location in cases no. 1, 2, and 3 where the values of *D* � *v* were less then 0.35 m<sup>2</sup> /s. In case no. 4 the car model dragged from its original location and sliding failure mode was noticed. The numerical results were compared with Australiana rainfall and runoff guidelines (2011) [32] and good agreement was noticed as shown in **Figure 18**. From the results it can be concluded that, numerical simulation using FLOW-3D can give good predictions and results related to vehicle instability limits. Several car models can be tested numerically flowing same steps under different road slops as well as under different orientations.

#### **6. Conclusions**

**Figures 16** and **17** show the relationship between water depth and buoyancy force with respect to the time for scaled and prototype models, respectively. The buoyancy force increased gradually with the depth increment and the values of floating depths were 3.7 cm (which can be scaled up to 37 cm) and 37.5 cm for scaled and prototype models, respectively. From both simulated parameters (buoyancy force and floating depth) it can be concluded that the numerical modeling by using FLOW-3D software gave accurate results and allowed to test vehicles in real scale.

*Flood Impact Mitigation and Resilience Enhancement*

*Relationship between the buoyancy force and car center of mass with time (prototype).*

*Relationship between the buoyancy force and water depth with time (1:10).*

*Relationship between the buoyancy force and water depth with time (prototype).*

**Figure 15.**

**Figure 16.**

**Figure 17.**

**188**

In the past, research on vehicle instabilities have been solely dedicated to stationary vehicles which are normally translated as vehicles parked on a road surface. A vehicle exposed to floodwater gets influenced by different hydrodynamic forces and prone to various instability modes. Outcomes on such modes are somehow recognized in the work on stationary vehicles, but the existing approaches possess a limited ability to communicate with road user with respect to complicated hydrodynamic and nature of flooding. In an attempt to prevent fatalities in commonly flooded or flood-prone areas, permanent structures are installed to warn users regarding the depth of the water at the flooded area. The existing flood monitoring system only focuses on water conditions in rivers or lake in order to determine risks associated with floods. The present invention, FLO-LOW relates to an online decision-making tool for road users to decide the likelihood on crossing low-lying areas that are prone to flooding. The flood risk assessment system provides a realtime monitoring of flood condition at flood-prone area for determining the hydrodynamic instability of a vehicle.

#### **7. Recognition**

FLO-LOW different individual component as well as the whole complete system have been submitted for Intellectual Property under three categories, namely Patent (PI 2017702574), Patent (PI 2019001397), Industrial Design (17-E0208-0101) and

Trademark (2019012521). FLO-LOW has won different international and national awards, namely ITEX'18, MRCIIE'18, INTEX'19, MIIEX'19, PECIPTA'19 and IDE4TE'19.

## **Acknowledgements**

The authors gratefully acknowledge the supports provided by Universiti Teknologi PETRONAS through Prototype Fund (015PBA-008) and hydraulic laboratory equipment utilized for this research.

## **Abbreviations and nomenclature**


**Author details**

**191**

Zahiraniza Mustaffa<sup>1</sup>

\*, Ebrahim Hamid Hussein Al-Qadami<sup>1</sup>

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

1 Department of Civil and Environmental Engineering, Universiti Teknologi

*Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities*

*DOI: http://dx.doi.org/10.5772/intechopen.92731*

2 Civil Engineering Department, Sir Syed University of Engineering and

Syed Muzzamil Hussain Shah<sup>2</sup> and Khamaruzaman Wan Yusof<sup>1</sup>

\*Address all correspondence to: zahiraniza@utp.edu.my

PETRONAS, Seri Iskandar, Perak, Malaysia

provided the original work is properly cited.

Technology, Karachi, Pakistan

,

*Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities DOI: http://dx.doi.org/10.5772/intechopen.92731*

## **Author details**

Trademark (2019012521). FLO-LOW has won different international and national awards, namely ITEX'18, MRCIIE'18, INTEX'19, MIIEX'19, PECIPTA'19 and

The authors gratefully acknowledge the supports provided by Universiti Teknologi PETRONAS through Prototype Fund (015PBA-008) and hydraulic labo-

*θ* angle between the tire vertical axes and the point where the tire no more

*b* horizontal distance between the tire vertical axes and the point where the

IDE4TE'19.

**Acknowledgements**

*v* flow velocity *FG* gravitational force *μ* friction coefficient *ρ* water density *CD* drag coefficient

*FR* friction force *FB* buoyancy force

*AL* area affected by lift force *Fg* vehicle curb weight *FRO* rolling resistance *R* resultant reaction

touching the ground

CFD computational fluid dynamics

FAM finite element method

tire no more touching the ground

*g* gravity *V* volume *FL* left force *CL* left coefficient

*r* tire reduce

**190**

*w* vehicle net weight *μRO* rolling coefficient *FDV* driving force *m* vehicle mass *a* acceleration *t* time *d* flow depth

ratory equipment utilized for this research.

*Flood Impact Mitigation and Resilience Enhancement*

*AD* normal projected area to the flow direction

**Abbreviations and nomenclature**

Zahiraniza Mustaffa<sup>1</sup> \*, Ebrahim Hamid Hussein Al-Qadami<sup>1</sup> , Syed Muzzamil Hussain Shah<sup>2</sup> and Khamaruzaman Wan Yusof<sup>1</sup>

1 Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia

2 Civil Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan

\*Address all correspondence to: zahiraniza@utp.edu.my

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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*Flood Impact Mitigation and Resilience Enhancement*

[10] Sant Llorenc des Cardassar flash

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## *Edited by Guangwei Huang*

The concept of resilience has been gaining momentum in various fields in recent years and has been used in various ways from a catch phrase to a cornerstone in theoretic development or practical operation. No matter how it is used, it does contribute one way or another to the refinement and application of the concept. This book focuses on the application of the resilience concept to flood disaster management. This book is a collection of research works conducted across the world and across sectors. Therefore, it is a good example of how different perspectives can catalyze our insight into complex flood-related issues. It can be considered valuable reading material for students, researchers, policymakers and practitioners, because it provides both the fundamentals and new development of resilience-based approaches and delivers a message that the goal of resilience-based flood management goes beyond disaster reduction.

Published in London, UK © 2021 IntechOpen © Mrkit99 / iStock

Flood Impact Mitigation and Resilience Enhancement

Flood Impact Mitigation

and Resilience Enhancement

*Edited by Guangwei Huang*