Sebastian Lindner, Stephan Kühnel, Hans Betke, & Stefan Sackmann. (2018). Simulating Spontaneous Volunteers – A Conceptual Model. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 159–169). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Recent disasters have revealed growing numbers of citizens who participate in responses to disasters. These so-called spontaneous unaffiliated on-site volunteers (SUVs) have become valuable resources for mitigating disaster scales. However, their self-coordination has also led to harm or putting themselves in danger. The necessity to coordinate SUVs has encouraged researchers to develop coordination approaches, yet testing, evaluating, and validating these approaches has been challenging, as doing so requires either real disasters or field tests. In practice, this is usually expensive, elaborate, and/or impossible, in part, to conduct. Simulating SUVs' behaviors using agent-based simulations seems promising to address this challenge. Therefore, this contribution presents a conceptual model that provides the basis for implementing SUV agents in simulation software to perform suitable simulations and to forecast citizens' behaviors under a given set of circumstances. To achieve adequate simulations, the conceptual model is based on the identification of 25 behavior-affecting attributes.
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Tanaporn Panrungsri, & Esther Sangiamkul. (2017). Business Intelligence Model for Disaster Management: A Case Study in Phuket, Thailand. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 727–738). Albi, France: Iscram.
Abstract: This research presents the conceptual Business Intelligence (BI) model for disaster management. BI can provide agility capacity for decision making in dynamic environment among different agencies. This project designs and develop a data warehouse using multi-dimensional model for severity analysis of flood and landslide in risk area using case study from Department of disaster prevention and mitigation (DDMP), Phuket, Thailand. The concept of BI can be applied for extremely heterogeneous data structures and data platform environment to improve data quality and expose to better decision-making for disaster management. In the next stage of this project, we will integrate more data sources from other agencies for example GIS data from Phuket land-use planning and flooding prediction model database. The result of this study will help organization deploy BI more effectively.
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Denis Barcaroli, Alex Coletti, Antonio De Nicola, Antonio Di Pietro, Luigi La Porta, Maurizio Pollino, et al. (2019). An Automatic Approach to Qualitative Risk Assessment in Metropolitan Areas. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Risk assessment aims at improving prevention and preparedness phases of the crisis management lifecycle.
Qualitative risk assessment of a system is important for risks identification and analysis by the various stakeholders and often requires multi-disciplinary knowledge. We present an automatic approach to qualitative
risk assessment in metropolitan areas using semantic techniques. In particular, users are provided with a computational support to identify and prioritize by relevance risks of city services, through generation of
semantic descriptions of risk situations. This approach is enabled by a software system consisting of: TERMINUS, a domain ontology representing city knowledge; WS-CREAM, a web service implementing risk identification and ranking functions; and CIPCast, a GIS-based Decision Support System with functions of risk
forecast due to natural hazards. Finally we present the results of a preliminary validation of the generated risks concerning some points of interest in two different areas of the city of Rome.
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Philippe Kruchten, Carson Woo, Kafui Monu, & Mandana Sotoodeh. (2007). A human-centered conceptual model of disasters affecting critical infrastructures. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 327–344). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Understanding the interdependencies of critical infrastructures (power, transport, communication, etc.) is essential in emergency preparedness and response in the face of disasters. Unfortunately, many factors (e.g., unwillingness to disclose or share critical data) prohibited the complete development of such an understanding. As an alternative solution, this paper presents a conceptual model-an ontology-of disasters affecting critical infrastructures. We bring humans into the loop and distinguish between the physical and social interdependencies between infrastructures, where the social layer deals with communication and coordination among representatives (either humans or intelligent agents) from the various critical infrastructures. We validated our conceptual model with people from several different critical infrastructures responsible for disasters management. We expect that this conceptual model can later be used by them as a common language to communicate, analyze, and simulate their interdependencies without having to disclose all critical and confidential data. We also derived tools from it.
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Starr Roxanne Hiltz, Jose J. Gonzalez, & Murray Turoff. (2013). ICT support and the effectiveness of decision making in disasters: A preliminary system dynamics model. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 668–673). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: A high level conceptual model is presented of factors hypothesized to be key determinants of the effectiveness of decision making in large scale disasters, grounded in the literature on disaster management. ICT robustness (including the use of social media) sensemaking, and the effectiveness of decision making processes by the multi-organizational Partially Distributed Teams that must cooperate are accorded key roles in the process model. The outcomes of the decision making processes modeled are decisions, in terms of timeliness and quality.
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