**3. Emerging technologies of disaster DSS**

102 Emerging Informatics – Innovative Concepts and Applications

specific reflection, a decision support framework is required for different stakeholders. Technological provisions for on-site or remote access solution concepts are not new in the use of DSS applications. Although some existing DSS solutions integrate GIS provisions, they lack appropriate process that are required to address concurrent data capturing for decision making and relevant decision sharing in users' context. Buzolic, Mladineo, and Knezic (2002) developed a DSS solution for disaster communications through decision processes in the phases of preparation, prevention and planning of a protection system from natural and other catastrophes, as well as in phases throughout interventions during an emergency situation in the telecommunications part. The DSS model is based on a GIS that only capture location specific necessary data. Using a combination of GIS and multi-criteria DSS approach, according to natural catastrophes such as floods, earthquakes, storms, and bushfires, data regarding the vulnerability of the telecommunications system are generated (Buzolic et al. 2002) in the system model. Similarly, Mirfenderesk (2009) described a DSS designed for mainly to assist in a post-disaster situation. However, this DSS model is used traditional technique for pre-disaster flood emergency planning. As a post-disaster measure it can identify vulnerable populations and assist in the evacuation of those at risk (Mirfenderesk, 2009). Rodríguez, Vitoriano and Montero (2009) developed a DSS for aiding the Humanitarian NGOs concerning the response to natural disasters. This DSS has been developed avoiding sophisticated methodologies that may exceed the infrastructural requirements and constraints of emergency management by NGOs. The used method is not technically novel enough to capture decision makers' context. A relatively simple two-level knowledge methodology is utilised in this approach that allows damage assessment of multiple disaster scenarios (Rodríguez et al. 2009). Apart from these DSS approaches, there some disaster management solution have been developed such as MIKE 11 (Designed by Danish Hydraulic Institute) (Kjelds and Müller, 2008) and SoKNOS (Service-Oriented ArchiteCtures Supporting Networks of Public Security- a service oriented architecture based solution design by the German federal government) (SoKNOS, 2011), but they are still in the embryonic or conception stage of design. None addresses the technical requirements of using business intelligence techniques. Therefore issues remain in designing a relevant decision process with support for policy making and operational levels for disaster impact assessments to meet emergency requirements for the

Drawing upon the above, in the proposed DSS solution, the architecture comprises several technological layers. The first layer collects disaster information from a range of different sensors or sources. This collected information is then processed through a business analytics method (Davenport and Harris, 2007) before storage in knowledge repositories. In the second layer, analysed results with context details are stored in knowledge repositories with their enhanced semantics. The *Protégé* based knowledge framework (Gennari et al. 2003) is employed for knowledge codifications through a controlled vocabulary principle. Finally, the third layer (called data fostering unit) handles the codified knowledge through different decision making policies or rules (for example, rules based decision making- Miah, Kerr and Gammack, 2009) in various decision making levels. Figure 1 illustrates more detail

The digital eco-systems research paradigm provides a powerful and broad methodological foundation for such a DSS solution design for addressing the information needs of different stakeholders. A digital ecosystem enables a platform that facilitates self organizing digital system solutions aimed at creating a digital environment for stakeholders in organizations

safety of humans and other living beings in emergencies.

descriptions.

DSS based applications are well- known and extensively applied in disaster management. Various technologies are used for instances, in natural disaster management, such as flood control and forescasting, hazard management and pre and post activities management. Table 1 illustrates various DSS design applications in previous studies.

There are many technologies that have been used in DSS application development for the purpose of disaster situation management. Bessis, Asimakopoulou and Xhafa (2011) highlighted potential use of next generation emerging technologies in managing disaster situations. The technologies are namely: *grid computing* (an information sharing solution that allows integrated, collaborative use of distributed computing resources including servers (nodes), networks, databases, and scientific tools managed by multiple organisations); *web services* (an information sharing infrastructure to provide stateless, persistent services and resolves distributed computing issues); *cloud computing* (a service solution that is defined as incorporating virtualisation and utility computing notions as a type of parallel and distributed system); *pervasive computing* (also known as ubiquitous computing, a means to enable resource computation and utilisation in a mobile or environmentally-embedded manner); and *crowd computing* (a service platform and sometimes called 'crowd-sourced' mobile phone data from citizens) (Bessis et al. 2011).Our findings suggest that there are three main approaches that are used for decision support design in different purpose specific disaster managements, namely: integrated dynamic model based, GIS based and cloud computing based approaches.

Asghar et al. (2006) described a dynamic integrated model for disaster management from their previous study on a single DSS design. The model handles specific decision-making needs through the use of combining more than one model. Literature in Asghar et al. (2006) suggested that different DSS systems are designed for various categories of disasters and most of the time DSSs are based on specific models and decision support needs. However, there are different needs of decision support in disaster management in that one single model may not be sufficient to cope with the problem situations (Asghar et al. 2006). Asghar's et al integrated model is decomposed into modular subroutines that are functionally independent in the problem space.

An Emerging Decision Support Systems Technology for Disastrous Actions Management 105

became interested in utilising the cloud computing platform for launching new services that met their client group demands through its stable infrastructure (Santos, Gummadi & Rodrigues, 2009). Many studies found cloud computing useful in public safety and disaster management applications. In theory, the approach supports a convergence of interactions

In disaster situations involves a number of entities such as disaster management authorities, teams and individuals from more than one location that are geographically distributed. The entities can be for example medical teams, rescue services, police, civil protection, fire, health and safety professionals, and ambulance services that will be required to communicate, cooperate and collaborate – in real time – in order to take appropriate decisions and actions (Bessin et al. 2011, Graves, 2004; Often et al., 2004). This implies that decision making for such combined action taking is rather multifarious due to diverse information processing from different sources. Carle et al. (2004) suggest that *"there are frequent quotes regarding the lack and inconsistent views of information shared in emergency operations"* (Bessin et al. 2011 p. 77). Carle et al. (2004) also reported that information exchange during an emergency situation is very important and can be very diverse and complex and at analysing information is very important, yet none of the current

Disaster managers play important roles in disaster situations. Asimakopoulou and Bessis (2010) and Bessis et al. (2010) note that disaster managers are required to identify the site of people and evaluate their present and projected impacts, because there is no real benefit in sending rescue team to a place if there is no one actually present. This implies that disaster managers need analysed information to make such a quick decision in an appropriate way. Because, it is important to urgently send the rescue team to a place where there is a great risk of someone or many injured. It is straightforward to construct many plausible scenarios where knowledge can be collected from various emerging technologies to the benefit of the disaster managers and the society. In other words, access to shared information regarding the number, whereabouts and health of people in an area struck by a disaster will significantly enhance the ability of disaster managers to respond timely to the reality of the situation. The aforementioned challenges are so vast and multifaceted that it is clearly

A framework for analysing disaster management activities is outlined by Wallace & De Balogh, (1985). The proposed framework uses pre and post event based activities for identifying scope of DSS development. The approach can be useful to guide the

As mentioned previously, a service oriented architecture based solution in the public security sector called SoKNOS (SoKNOS, 2011) has been developed, in which different technological and user oriented objectives will be investigated such as machine-readable semantics, user-friendly workplace, highly-reliable system behavior and integral data processing. Another objective is machine readable semantics to provide relevant services to user contexts using appropriate machine readable coding in developed technology. Further objective is the user-friendly workplace, which concerns fulfilling decision makers' desires through technological features. This aspect relates to how the technology will accommodate current user's problem scenario and information flow appropriate to particular situation. To

and decision making in a broader community perspective.

**4. Disaster management aspects** 

technologies support such needs.

insufficient to address all of them here.

implementation of DSS technologies for specific purpose.


Table 1. Different purpose and technologies used for disaster management DSS development

Gupta (2011) described various stages involved in the preparation of GIS based DSS for disaster management in India. The solution includes the development of an integrated geodatabase that consists of various thematic maps, demographic data, socio-economic data and infrastructural facilities at remote level (Gupta, 2011). Gupta's approach uses a menu driven system that is used by administrators who may not have in-depth knowledge of working in GIS. Adityam and Sarkar (1998) also describe a GIS-based DSS for Indian cities affected by cyclones. However, the approach is focused on disaster preparedness and planning through the use of GIS technologies.

The term "cloud computing" has become popular due to its contribution to shared computing applications. Fitzgerald and Dennis (2010) described cloud based design as a "*circuit-switched service architecture*" that is easier to implement for organisations because *"they move the burden of network design and management inside the cloud"* (p. 297). As such, cloud computing provision has been used as a modern architecture of shared computing services. When Amazon.com provided web-based utility services, many service providers became interested in utilising the cloud computing platform for launching new services that met their client group demands through its stable infrastructure (Santos, Gummadi & Rodrigues, 2009). Many studies found cloud computing useful in public safety and disaster management applications. In theory, the approach supports a convergence of interactions and decision making in a broader community perspective.
