**3.1 Process description: impact and influence determination**

Over the years, the fast development of map services [29] and volunteered geographic information (VGI) [30] has provided a huge number of geo-tagged images. This data source has given information on every corner of a city and has been enabling broader and more in-depth quantitative research in related fields. These data improve the understanding of the dynamic and physical features of the city by identifying landmark [31], detecting urban identities [32, 33], assessing the living environment inequality [34], and modeling human activities [35] and popular places [36]. Also, they provide information on the social and physical structures of dynamic metropolitan environments [36, 37].

The MIT Media Lab launched the "Place Pulse" program in 2013, which is a data collection platform that enables volunteers to take part in the urban perception rating experiment. By the end of 2016, the MIT Place Pulse dataset had collected 1,170,000 pairwise comparisons from 81,630 online participants for 110,988 cityscape images. Given this dataset and advances in ML techniques, many studies have tried to analyze human perceptions of urban appearance [34, 38–41].

Since previous approaches use low or mid-level image attributes, they have problem in extracting high-level information about the natural image. Some examples of these attributes are: Gist, SIFT- Fisher Vectors, DeCAF features [41], geometric classification map, color Histograms, HOG2x2, and Dense SIFT [40].

For instance, according to building models, SVM and Linear Regression were used in [41] to predict image labels. Support Vector Regression was used in [40], Ranking SVM it has been used in [42], and several convolutional neural network (CNN) based approaches were used in [39, 41, 42].

Among the various image representations and models, approaches that uses deep convolutional neural network (DCNN), have outperformed conventional methods to a large extent. In [39], authors introduce a DCNN model that is based on the Deep Residual Network (ResNet) [43], which won the first place in the ImageNet Large Scale Visual Recognition Competition [44].

Recently, a shift-invariant and hierarchical model has emerged in the form of DCNN, because of the availability of large-scale annotated datasets and the rapid development of high-performance computing systems. In some research, DCNN was employed to conduct human perception modeling and prediction. Due to its powerful capability to learn and represent automatic image feature, this model has attracted a lot of attention and achieved great success in multiple fields, including speech recognition [45], natural language processing [46], and visual object detection [43, 47].

A very deep convolutional neural network is hard to train and optimize because of disappearance gradients and the curse of dimensionality [43, 48]. ResNet is known as an acceptable attempt to address this problem. It was designed to learn the residual functions with regard to the layer inputs rather than learning the unreferenced functions [43].

In [49], a data-driven ML approach that measures how people perceive a place in a large-scale urban region, was proposed. In particular, a deep learning model, that had been trained with millions of human ratings of street-level imagery, was used to predict human perceptions of a street view image. The model achieved a high accuracy rate in predicting six human perceptual indicators. These indicators are: beautiful, boring, depressing, lively, safe and wealthy. It could help to map the distribution of the city-wide human perception for a new urban area. Besides, to determine the visual points that may cause a place to be perceived as various perceptions, a series of statistical analyses was performed. From the 150 object categories that had been segmented from the images of the street view, many

Urban expansion modeling became widespread in the 1960s [9] and have accelerated with the developments of the technologies like Remote Sensing (RS) and GIS. Today, RS has been widely recognized as an essential tool for urban planning, management and design due to allows to get spatiotemporal data that are necessary for modeling environmental impacts, urban expansion and population growth. Particularly, the benefits of satellite-based image data have attracted attention of the scientific studies on urban expansion and environment [10, 11]. RS enables the collection of spatial details data for large areas at different time intervals; therefore, it provides a unique perspective on revealing the spatial and temporal dynamics of the change process in the land use and urban expansion [12]. GIS technology is described as an effective tool for identifying and monitoring the land cover change

The dynamic modeling via GIS as a tool for urban simulation has rapidly gained popularity [7, 17]. The application of ML models has increased noticeably in RS filed

The useful application of artificial neural networks (ANNs) in interpreting spatial resource information have been proven i.e. one of the most common are backpropagation neural networks, which are widely used by spatial planners. However, to improve the usability of ANNs for map-based applications, a more efficient method for communicating between the GIS and a trained ANN, is critical. When ANNs and GIS are used together for many applications to improve decision-making quality. The ANN design will consist of numerous layers, all of which can have different weights. The training process of an ANN involves changing the weightings over time until as it is desired the network reaches the static or optimum firing state. After training, an ANN can be used for applications effectively and consistently. Through the application of an ANN, GIS professionals can add another dimension to their spatial capabilities. In some research, a combination of neural networks with remote sensing image data for mapping the urbanization dynamics,

As another helpful technic, recently evolutionary algorithms have been used to tackle a variety of complex computation and optimization problems, such as natural language processing [23], route finding [24] and image processing [25]. One of the most important applications of evolutionary algorithms is in the field of GIS [26–28]. It should be noted that generally these algorithms are not fast in compari-

All in all, there are two main ways for satellite-image processing that each of them has its own advantages and disadvantages. Sometimes there is high-resolution data (satellite images) so processing this amount of data would take a lot of time, with high accuracy. Some other cases, depending on the issue the resolution is not

son with other ML algorithms and their main usage is for optimization.

good, but the measurement methods are highly qualified.

due to the increased availability of powerful and flexible ML software and

improvements in computing performance [18, 19].

at different scales [13–16].

*Built-up urban area population in 2019.*

*Geographic Information Systems in Geospatial Intelligence*

**Figure 2.**

has been proposed [20–22].

**34**

objects were identified as being positively or negatively correlated with each of the indicators. The mentioned results helped urban planners and researchers to take a step toward getting the interactions of the place sentiments and semantics.

Decision-makers of cities need improved information, that regularly updated, about human behavior and perceptions and how they relate to climate change, both globally and locally. Considering human behavior in cities and linking it to downscaled climate projections and remotely sensed observations of urban form, land cover, land-use patterns and social-demographic information from national and international databases, has the potential for improved decision-making to drive a much more nuanced and highly spatially resolved platform. Over the past decade, with the advancement of the digital social sciences and big data, as well as the increasing use of social media data (SMD) in geographical studies, new opportunities have emerged for augmenting urban systems and climate impacts research and

*A Review of the Machine Learning in GIS for Megacities Application*

*DOI: http://dx.doi.org/10.5772/intechopen.94033*

Geocoded SMD, which comes from social media users (e.g., Twitter, Facebook, Instagram) opens up a significant new opportunity to fill data gaps and address many of the obstacles that prevent researchers and practitioners from understanding the human behavior component of urban system dynamics and climate change. SMD and other big data let researchers to ask a wide range of spatially explicit questions at an unprecedented scale. Most of the time, social media allows users to manually select the location from where they post a message, or automatically adds it via geolocation tracking services. However, at present, geo-located tweets and Flickr photographs represent a small portion of the overall volume of SMD (e.g., only %1 of all tweets are tweets that geocoded via GPS constitute) [58] the sheer quantity of these data makes them worth investigating. Geotagged tweets can reinforce traditional control data (e.g. remotely sensed images, roads, parcels). For instance, for modeling population distribution, geo-located Twitter messages can

Research using geo-located SMD is also starting to take shape to study socioeconomic disparities and their relationship to climate impacts in cities. For example, crowd-sourced data from Foursquare users in London, had been used to be a reliable proxy for the localization of income variability and highlighting places

Yet, mapping based on data that are demographically unrepresentative, can also regenerate spatial segregation and give an unfair picture of the places which matter citywide [61]. The same is true for global-scale analyses. The amount of geocoded tweets widely varies among nations. The United States and Brazil are some of the countries whit the highest proportion of geocoded and non-geocoded tweets, while countries such as Norway and Denmark record considerably lower

The emergence of various types of big data provides interesting options for evaluating how people use and respond to urban events, policies, programs and designs to adapt and mitigate climate change. New types of data can be an important source for examining the use, value and social equity of specific spaces in the city, that provide refuge during climate driven extreme events, such as parks, vacant areas and open spaces that can provide, like cooling during heat waves. Working with big data can provide opportunities for multi-year to decadal datasets to understand the interactions between human and nature in the city and could be crucial to evaluate progress on examining influences of climate change and of

Various sources of big data have already been helpful for awareness of disaster risk management and climate adaptation planning. In [63], authors for assessing the desired location and capacity of flood evacuation shelters, used volunteered geographic information through SMD as a source; while, in [64], researchers used SMD sourced observations of flooding to develop a method for estimating flood extent in Jakarta. In addition, following the devastating impact of Hurricane Sandy in New

expanding them [57].

serve as control data [59].

values [62].

**37**

which are more at risk across the city [60].

mitigation options in cities [57].

Big data are voluminous and complex data of different qualities, that have the potential to generate new hypotheses and new methods for understanding interactions between social, biophysical and infrastructure domains of complex urban systems that face the challenges of climate change [50, 51].

The movement of people tracked through cell phones is an example of crowdsourced and big data, which offers manifold new possibilities for assessing the city's inner workings, and the availability, quality and quantity of data is evolving, rapidly (**Figure 3**). Crowd-sourced information can be used as a reliable proxy, with much better resolution and replication, for more traditional methods of empirical social survey [53].

Similar analyses of social media provide the opportunity to complement the existing traditional ways of collecting information about human behavior in cities, which can be brought together with other sources of biophysical and infrastructural data, especially in spatial formats, collected through GIS [54, 55].

Big data can also come from urban hotlines, city planning offices, tax assessor databases, records about utility use and repair, and the rapid emergence of sensors and instrumented buildings, ecological spaces and even roads [56].

In **Figure 3**, The direction of change is shown by color, where I equals warmer and wetter; II colder and wetter; III colder and drier; and IV warmer and drier conditions. The direction of change is measured with the Euclidean distance in the 2D space including temperature and precipitation change. The classification of the magnitude of change corresponds to quartiles.

The usefulness of big data for understanding urban systems such as efficacy of climate solutions and climate impacts will only increase with time [56].

#### **Figure 3.**

*Mapping direction of temperature change from 1901 until 2014 and rainfall from 1901 until 2013 in cities [52].*

#### *A Review of the Machine Learning in GIS for Megacities Application DOI: http://dx.doi.org/10.5772/intechopen.94033*

objects were identified as being positively or negatively correlated with each of the indicators. The mentioned results helped urban planners and researchers to take a step toward getting the interactions of the place sentiments and semantics.

Big data are voluminous and complex data of different qualities, that have the potential to generate new hypotheses and new methods for understanding interactions between social, biophysical and infrastructure domains of complex urban

The movement of people tracked through cell phones is an example of crowdsourced and big data, which offers manifold new possibilities for assessing the city's inner workings, and the availability, quality and quantity of data is evolving, rapidly (**Figure 3**). Crowd-sourced information can be used as a reliable proxy, with much better resolution and replication, for more traditional methods of empirical social

Similar analyses of social media provide the opportunity to complement the existing traditional ways of collecting information about human behavior in cities, which can be brought together with other sources of biophysical and infrastructural

Big data can also come from urban hotlines, city planning offices, tax assessor databases, records about utility use and repair, and the rapid emergence of sensors

In **Figure 3**, The direction of change is shown by color, where I equals warmer and wetter; II colder and wetter; III colder and drier; and IV warmer and drier conditions. The direction of change is measured with the Euclidean distance in the 2D space including temperature and precipitation change. The classification of the

The usefulness of big data for understanding urban systems such as efficacy

of climate solutions and climate impacts will only increase with time [56].

*Mapping direction of temperature change from 1901 until 2014 and rainfall from 1901 until 2013*

systems that face the challenges of climate change [50, 51].

*Geographic Information Systems in Geospatial Intelligence*

data, especially in spatial formats, collected through GIS [54, 55].

and instrumented buildings, ecological spaces and even roads [56].

magnitude of change corresponds to quartiles.

survey [53].

**Figure 3.**

**36**

*in cities [52].*

Decision-makers of cities need improved information, that regularly updated, about human behavior and perceptions and how they relate to climate change, both globally and locally. Considering human behavior in cities and linking it to downscaled climate projections and remotely sensed observations of urban form, land cover, land-use patterns and social-demographic information from national and international databases, has the potential for improved decision-making to drive a much more nuanced and highly spatially resolved platform. Over the past decade, with the advancement of the digital social sciences and big data, as well as the increasing use of social media data (SMD) in geographical studies, new opportunities have emerged for augmenting urban systems and climate impacts research and expanding them [57].

Geocoded SMD, which comes from social media users (e.g., Twitter, Facebook, Instagram) opens up a significant new opportunity to fill data gaps and address many of the obstacles that prevent researchers and practitioners from understanding the human behavior component of urban system dynamics and climate change.

SMD and other big data let researchers to ask a wide range of spatially explicit questions at an unprecedented scale. Most of the time, social media allows users to manually select the location from where they post a message, or automatically adds it via geolocation tracking services. However, at present, geo-located tweets and Flickr photographs represent a small portion of the overall volume of SMD (e.g., only %1 of all tweets are tweets that geocoded via GPS constitute) [58] the sheer quantity of these data makes them worth investigating. Geotagged tweets can reinforce traditional control data (e.g. remotely sensed images, roads, parcels). For instance, for modeling population distribution, geo-located Twitter messages can serve as control data [59].

Research using geo-located SMD is also starting to take shape to study socioeconomic disparities and their relationship to climate impacts in cities. For example, crowd-sourced data from Foursquare users in London, had been used to be a reliable proxy for the localization of income variability and highlighting places which are more at risk across the city [60].

Yet, mapping based on data that are demographically unrepresentative, can also regenerate spatial segregation and give an unfair picture of the places which matter citywide [61]. The same is true for global-scale analyses. The amount of geocoded tweets widely varies among nations. The United States and Brazil are some of the countries whit the highest proportion of geocoded and non-geocoded tweets, while countries such as Norway and Denmark record considerably lower values [62].

The emergence of various types of big data provides interesting options for evaluating how people use and respond to urban events, policies, programs and designs to adapt and mitigate climate change. New types of data can be an important source for examining the use, value and social equity of specific spaces in the city, that provide refuge during climate driven extreme events, such as parks, vacant areas and open spaces that can provide, like cooling during heat waves. Working with big data can provide opportunities for multi-year to decadal datasets to understand the interactions between human and nature in the city and could be crucial to evaluate progress on examining influences of climate change and of mitigation options in cities [57].

Various sources of big data have already been helpful for awareness of disaster risk management and climate adaptation planning. In [63], authors for assessing the desired location and capacity of flood evacuation shelters, used volunteered geographic information through SMD as a source; while, in [64], researchers used SMD sourced observations of flooding to develop a method for estimating flood extent in Jakarta. In addition, following the devastating impact of Hurricane Sandy in New

York City, scientists used SMD in Twitter to reveal the geographies of a range of social processes and actions that happened shortly after the event [65].

As mentioned before, authors' work in [68] is very instructive. Using a combination of CNN, daytime satellite imagery and nightlight data, they predict poverty in five African countries at a village scale. For this purpose, they did their analysis in three steps. In the first step the CNN is trained on ImageNet [74] to learn how to recognize visual attributes like edges and corners. Second, it was well tuned so as to be able to predict intensities of the night-time in daytime images. Nightlights are a universally consistent poverty predictor. Therefore, the model was trained to focus on the aspects in daytime imagery, which are relevant to poverty estimation. In the third and final step, socio-economic survey data was used to train ridge-regression models on both household surveys and the image features from the previous two steps. Their approach used night-time data as a globally consistent, but very noisy proxy for poverty in an intermediate step and eventually explains %37–55 of average household consumption, and %55–75 of the variation in average household asset

*A Review of the Machine Learning in GIS for Megacities Application*

*DOI: http://dx.doi.org/10.5772/intechopen.94033*

wealth. While it used publicly available data, it delivered better results than cellphone-based studies and outperforms products that rely solely on nightlights. Recently another study used data extracted from Google Street View images and ML methods, such as v-support regression and feature extraction, to estimate high income areas in US cities [38]. From another perspective, phone records were used to reveal detailed mobility patterns for improving the understanding of travel

An example of the activity recognition task is transportation mode detection can be in which data from smartphone sensors carried by users are employed to deduce what transportation mode the individuals have used. Microelectromechanical systems (MEMS), such as gyroscopes and accelerometers are embedded in most smartphone devices [76] from which the data can be obtained at high frequencies. Nowadays, smartphones have powerful sensors like Global Positioning System

(GPS), accelerometer, light sensors, gyroscope, etc. Having such sensors that embedded in a small device carried in all life activities has allowed researchers to investigate new research areas. Some of the benefits of these smart devices include ability to send and receive data through various ways (e.g. Wi-Fi/cellular network/ Bluetooth), ubiquity and processing data [77]. The knowledge of individuals' transport mode can be adopted in many applications and also can facilitate several tasks,

1.Knowing transportation mode is a necessary part of urban planning for

transportation, which is usually examined through questionnaires or telephone interviews or travel diaries [77, 78]. Most of the time, this traditional method of polling is inaccurate, expensive, limited to a specific area, and not up-to-

2.As environmental applications, by obtaining the transport mode, the carbon footprint and the amount of calories burnt of individuals can be determined. Besides, health situation and physical activities can be monitored, the risk exposure can be tracked, and the environmental influences of one's activities

3.Other applications such as giving real-time information to users with the knowledge of speed and transport mode from the them as probes [77, 81], offering individuals with personalized messages and advertisements based on

behavior and traffic management [75].

**3.2 High-precision measurement**

as follows:

**39**

date [79].

can be examined [80].

their transportation mode [77].

In another research, Twitter data collected have been used during the devastating Sendai earthquake in Japan to assess social networks and build a database to study the human landscape of post-disaster effects [66]. Understanding interactions between climate change and fire prone landscapes is another major concern for adapting with climate change and for reducing the disaster risk. In [67], authors were able to use SMD to evaluate spatial patterns of situational awareness during the Horsethief Canyon Fire in Wyoming, besides they demonstrated the usefulness of SMD for actionable content during a crisis.

Another promising route which has been used in previous research, is the combination of satellite data with other datasets and analyzing it by ML. For example, in [68], authors proposed an accurate, scalable and cheap method for estimating consumption expenditure and asset wealth from high-resolution satellite imagery. In this research, using satellite data from five African countries: Nigeria, Tanzania, Uganda, Malawi, and Rwanda, they showed how a CNNN can be trained to detect image features that can explain up to 75% of the variation in local-level economic outcomes, which result in estimation of poverty levels via satellite data.

Another point is that, big data can become a central tool for online monitoring of urban risks and climate policies, made possible by sensor-based cities and the large amounts of data typically generated by their residents through social media. Applications include [69]:


ML techniques, especially neural networks, are powerful tools for multidimensional and complex big data analysis, where complexity needs to be reduced to understand its main drivers [70]. CNNs work well to classify images [71], and are widely used to evaluate land-use patterns [72]. Some researchers have gone even further and combined this approach with the socio-economic data analysis [68, 73].

#### *A Review of the Machine Learning in GIS for Megacities Application DOI: http://dx.doi.org/10.5772/intechopen.94033*

As mentioned before, authors' work in [68] is very instructive. Using a combination of CNN, daytime satellite imagery and nightlight data, they predict poverty in five African countries at a village scale. For this purpose, they did their analysis in three steps. In the first step the CNN is trained on ImageNet [74] to learn how to recognize visual attributes like edges and corners. Second, it was well tuned so as to be able to predict intensities of the night-time in daytime images. Nightlights are a universally consistent poverty predictor. Therefore, the model was trained to focus on the aspects in daytime imagery, which are relevant to poverty estimation. In the third and final step, socio-economic survey data was used to train ridge-regression models on both household surveys and the image features from the previous two steps. Their approach used night-time data as a globally consistent, but very noisy proxy for poverty in an intermediate step and eventually explains %37–55 of average household consumption, and %55–75 of the variation in average household asset wealth. While it used publicly available data, it delivered better results than cellphone-based studies and outperforms products that rely solely on nightlights.

Recently another study used data extracted from Google Street View images and ML methods, such as v-support regression and feature extraction, to estimate high income areas in US cities [38]. From another perspective, phone records were used to reveal detailed mobility patterns for improving the understanding of travel behavior and traffic management [75].

#### **3.2 High-precision measurement**

York City, scientists used SMD in Twitter to reveal the geographies of a range of

In another research, Twitter data collected have been used during the devastating Sendai earthquake in Japan to assess social networks and build a database to study the human landscape of post-disaster effects [66]. Understanding interactions between climate change and fire prone landscapes is another major concern for adapting with climate change and for reducing the disaster risk. In [67], authors were able to use SMD to evaluate spatial patterns of situational awareness during the Horsethief Canyon Fire in Wyoming, besides they demonstrated the usefulness

Another promising route which has been used in previous research, is the combination of satellite data with other datasets and analyzing it by ML. For example, in [68], authors proposed an accurate, scalable and cheap method for estimating consumption expenditure and asset wealth from high-resolution satellite imagery. In this research, using satellite data from five African countries: Nigeria, Tanzania, Uganda, Malawi, and Rwanda, they showed how a CNNN can be trained to detect image features that can explain up to 75% of the variation in local-level economic

Another point is that, big data can become a central tool for online monitoring of urban risks and climate policies, made possible by sensor-based cities and the large amounts of data typically generated by their residents through social media. Appli-

• Use real-time data extracted from local weather stations, rainfall and sewer gauges to collect real-time data in hydrodynamic models for improved flood

• Combine local high frequency observations, with regional monitoring and forecasts, along with tracking of geospatial social messaging (e.g., posts about occurring events) to provide improved early warning about potential effects;

• Use image processed CCTV feeds to understand the risks, for instance, water surface locations and social media feeds to validate in real-time the emergent

• Integrate spatially heterogeneous sensor data from flows and movements with

• Apply knowledge from previous events, like modeling result sets of both risks and impacts, to improve pre-response event from the site to the city-scale for

ML techniques, especially neural networks, are powerful tools for multidimensional and complex big data analysis, where complexity needs to be reduced to understand its main drivers [70]. CNNs work well to classify images [71], and are widely used to evaluate land-use patterns [72]. Some researchers have gone even further and combined this approach with the socio-economic data

geospatial social messaging, CCTV and other data to reach a better

• Combine CCTV monitoring with social media data feeds to improve understandings of citizen reaction and response to emerging impacts for

understanding of the temporal dynamics of impacts;

optimized hazard mitigation and planning in future;

social processes and actions that happened shortly after the event [65].

outcomes, which result in estimation of poverty levels via satellite data.

of SMD for actionable content during a crisis.

*Geographic Information Systems in Geospatial Intelligence*

cations include [69]:

prediction;

the flooding patterns;

future events.

analysis [68, 73].

**38**

An example of the activity recognition task is transportation mode detection can be in which data from smartphone sensors carried by users are employed to deduce what transportation mode the individuals have used. Microelectromechanical systems (MEMS), such as gyroscopes and accelerometers are embedded in most smartphone devices [76] from which the data can be obtained at high frequencies.

Nowadays, smartphones have powerful sensors like Global Positioning System (GPS), accelerometer, light sensors, gyroscope, etc. Having such sensors that embedded in a small device carried in all life activities has allowed researchers to investigate new research areas. Some of the benefits of these smart devices include ability to send and receive data through various ways (e.g. Wi-Fi/cellular network/ Bluetooth), ubiquity and processing data [77]. The knowledge of individuals' transport mode can be adopted in many applications and also can facilitate several tasks, as follows:


Several studies have used GPS data for classification purposes. However, using GPS sensors have some limitations, such as: in shielded areas like tunnels, GPS information is not available and the GPS signals may be lost especially in high dense locations, which results in erroneous position information. In addition, the GPS sensor consumes power a lot, so sometimes users turn it off to save the battery [79, 81]. Some research focus on developing detection models using ML techniques and data obtained from smartphone sensors like gyroscope, accelerometer and rotation vector, without GPS data [82]. In this way, it has the advantage of considering multiple sensors even without using GPS, the transportation modes can be identified.

The goals of data quality metrics are multi-dimensional. Indeed, they can set information quality objectives for data creators and managers to achieve, set standards for data to be produced, acquired and curated, and introduce measurement

These metrics include rules that determine the thresholds of meeting appropriate professional expectations and govern the measurement of data quality aspects and levels. In order to configure and organize the rules, a basic structure is needed to distinguish the transformation process from data quality expectations to a set of

Defining dimensions of data quality metrics can meet some purposes. Most of the time, the dimensions are classified according to accepted standard of scholarly activities within an academic discipline as well as other related disciplines that use the data. Scientists have developed several sets of data quality dimensions [85]. The dimension categories differ from each other, according to the academic field(s) in which data are regulated or by the different researchers' understanding and preference. Not only their dimensions are categorized differently among scholars, but also their definitions vary according mostly to different types of data. In practice, variations exist, such as integrity may be described in a different way to measurement adjusted strategies, and accuracy may be calculated at different levels

Landslide susceptibility mapping (LSM) is a prime step in implementing the immediate disaster management planning and risk mitigation measures. All susceptibility models must be verified for their predictions accuracy. An unverified prediction model and susceptibility maps are nonetheless meaningless and hence do not have any scientific significance. The issue of LSM validation have tackled by

Several LSM approaches have been developed and described in numerous papers. These approaches are mainly divided into three groups: heuristic, deter-

The heuristic techniques are based on the expert's knowledge to group landslideprone areas into several ranks from high to low classes. It is often used for susceptibility mapping in large areas. While, deterministic techniques rely on numerical modeling of the physical mechanism that controls slope failure. However, they are not suitable for a large-scale mapping, due to their problematic and impractical need for a huge array of data, namely rock mechanical properties, the wetness and soil saturation and soil depth. Statistical and probabilistic techniques including bivariate, multivariate statistical methods, certainty factor, as well as knowledgebased techniques such as ANNs and fuzzy logic approaches are promising methods

In most cases, the models are tested with an independent set of data, which was not used for training the model. In [88], authors reported a three following approach to obtain an independent sample of the landslide for validation purpose [87].

1.From the entire landslide inventory map of the study area, two sets of randomly divided landslide polygons should be created, one for the

2. In a part of the whole study area, the susceptibility analysis should be

performed; the obtained result should be tested in another part, distinctly with

3.The analysis should be performed using landslides happened in specific period and validation should be carried using landslides occurred in a different

susceptibility analysis and one for validation the models;

applicable claims and to prevent unprofessional conduct [84].

*A Review of the Machine Learning in GIS for Megacities Application*

methods for quality judgment.

*DOI: http://dx.doi.org/10.5772/intechopen.94033*

of explanation [85].

many studies [86].

ministic and statistical techniques.

for predicting the landslides [87].

different landslides;

**41**
