Rebeca Barros, Pedro Kislansky, Laís Salvador, Reinaldo Almeida, Matthias Breyer, & Laia Gasparin. (2015). EDXL-RESCUER ontology: Conceptual Model for Semantic Integration. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: This paper describes an ontology created for the RESCUER[1] (Reliable and Smart Crowdsourcing Solution for Emergency and Crisis Management), a project funded by the European Union and the Brazilian Ministry of Science, Technology and Innovation, it uses crowdsourcing information for supporting Industrial Parks (InPa) and Security Forces during an emergency situation. The proposal, EDXL-RESCUER ontology, is based on EDXL (Emergency Data Exchange Language), and it aims to be the RESCUER conceptual model related to the coordinating and exchanging of information with legacy systems. The ontology was evaluated with end users during a workshop and the results show that EDXL-RESCUER is adequate for Emergency and Crisis domain in InPa and Security forces contexts.
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Hélène Soubaras, & Juliette Mattioli. (2007). Injury worsening risk modeling and rescue emergency analysis in a disaster. 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. 1–5). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In a crisis with casualties, while there is no medical intervention, the severity of the injuries increases, and some people may die. Since the number of rescuers is limited, it is necessary to perform a planning and a deployment of this resource on the basis of a risk criterion illustrating the potential increase of the number of casualties at each point of the concerned area. Emergency planning is still a poorly developed science [3]. This paper provides a dynamical model for the number of casualties, inspired from the Verhulst model classically used for biological systems [5], to evaluate this risk criterion as a function of future time. It calculates the evolution of the number of unrescued casualties, the number of dead people, and the number of rescued people, as a function of the number of rescuers. Numerical results are shown.
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