Jorge Vargas, Jonatan Rojas, Alejandra Inga, Wilder Mantilla, Hulber Añasco, Melanie Fatsia Basurto, et al. (2016). Towards Reliable Recurrent Disaster Forecasting Methods: Peruvian Earthquake Case. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: We are interested in recurrent disaster forecasts; these are events such as annual cyclones in the Caribbean, earthquakes along the Ring of Fire and so on. These crises, even small- or medium-sized, are, in fact, critical for the emergency response of humanitarian organizations inasmuch as the sum of casualties and losses attained are as deadly as those that are considered exceptional. The aim of our research is to show that it is possible to use traditional forecasting methods such as: causal methods (which include the use of linear regression functions, non-linear, multivariate, etc.), time series (which include simple moving average, weighted moving average, exponential smoothing, trend-adjusted exponential smoothing, etc.) and so on, if the historical data keeps, among other criteria, its patterns, frequency, and magnitude, in a sustainable manner. Finally, an example to forecast recurrent earthquakes in Peru is presented.
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Josey Chacko, Christopher Zobel, & Loren Rees. (2018). Challenges of Modeling Community-Driven Disaster Operations Management in Disaster Recurrent Areas: The Example of Portsmouth, Virginia. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1022–1029). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Although one of the dominant paradigms in managing disaster operations is that of modeling decisions around the activities of humanitarian organizations, recent literature has highlighted the importance of managing disaster operations from the perspective of the affected community. Modeling community-driven disaster operations has a unique set of challenges, however, several of which are highlighted in this research effort. These include engaging the community and coordinating amongst multiple decision makers, defining a clear community objective, and planning with long decision horizons. Using the urban area of Portsmouth, Virginia as a case study, this work in progress paper demonstrates a decision approach which addresses these critical elements of community-driven disaster operations management.
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