ISCRAM Digital Library -- Query Results

Simone De Kleermaeker, & Jan Verkade. (2013). A decision support system for effective use of probability forecasts. 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. 290–295). KIT; Baden-Baden: Karlsruher Institut fur Technologie. Abstract: Often, water management decisions are based on hydrological forecasts, which are affected by inherent uncertainties. It is increasingly common for forecasters to make explicit estimates of these uncertainties. Associated benefits include the decision makers' increased awareness of forecasting uncertainties and the potential for risk-based decision-making. Also, a more strict separation of responsibilities between forecasters and decision maker can be made. A recent study identified some issues related to the effective use of probability forecasts. These add a dimension to an already multi-dimensional problem, making it increasingly difficult for decision makers to extract relevant information from a forecast. Secondly, while probability forecasts provide a necessary ingredient for risk-based decision making, other ingredients may not be fully known, including estimates of flood damage and costs and effect of damage reducing measures. Here, we present suggestions for resolving these issues and the integration of those solutions in a prototype decision support system (DSS). A pathway for further development is outlined. Keywords: Artificial intelligence; Decision support systems; Forecasting; Hydrology; Information systems; Uncertainty analysis; Water management; Decision support system (dss); Hydrological forecast; Management decisions; Multidimensional problems; Predictive uncertainty; Probabilistic forecasts; Probability forecasts; Risk-based decisions; Decision making Track: Decision Support Systems http://idl.iscram.org/show.php?record=432
Tom Ritchey. (2006). Modeling multi-hazard disaster reduction strategies with computer-Aided morphological analysis. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 339–346). Newark, NJ: Royal Flemish Academy of Belgium. Abstract: Disaster Risk Management (DRM) is a multi-dimensional problem complex requiring knowledge and experience from a wide range of disciplines. It also requires a methodology which can collate and organize this knowledge in an effective, transparent manner. Towards this end, seven specialists from the social, natural and engineering sciences collaborated in a facilitated workshop in order to develop a prototype multi-hazard disaster reduction model. The model, developed with computer-Aided morphological analysis (MA), makes it possible to identify and compare risk reduction strategies, and preparedness and mitigation measures, for different types of hazards. Due to time constraints, the model is neither complete nor accurate-but only represents a proof-of-principle. The workshop was sponsored by the Earthquake Disaster Mitigation Research Center (EDM) in Kobe, in January, 2005. Keywords: Disaster prevention; Disasters; Information systems; Linguistics; Morphology; Risk assessment; Disaster reduction; Disaster reduction strategy; Engineering science; Knowledge and experience; Mitigation measures; Morphological analysis; Multidimensional problems; Proof of principles; Hazards Track: VISUALIZATION IN EMERGENCY MANAGEMENT http://idl.iscram.org/show.php?record=879

Abstract: Often, water management decisions are based on hydrological forecasts, which are affected by inherent uncertainties. It is increasingly common for forecasters to make explicit estimates of these uncertainties. Associated benefits include the decision makers' increased awareness of forecasting uncertainties and the potential for risk-based decision-making. Also, a more strict separation of responsibilities between forecasters and decision maker can be made. A recent study identified some issues related to the effective use of probability forecasts. These add a dimension to an already multi-dimensional problem, making it increasingly difficult for decision makers to extract relevant information from a forecast. Secondly, while probability forecasts provide a necessary ingredient for risk-based decision making, other ingredients may not be fully known, including estimates of flood damage and costs and effect of damage reducing measures. Here, we present suggestions for resolving these issues and the integration of those solutions in a prototype decision support system (DSS). A pathway for further development is outlined.

Keywords: Artificial intelligence; Decision support systems; Forecasting; Hydrology; Information systems; Uncertainty analysis; Water management; Decision support system (dss); Hydrological forecast; Management decisions; Multidimensional problems; Predictive uncertainty; Probabilistic forecasts; Probability forecasts; Risk-based decisions; Decision making

Track: Decision Support Systems

http://idl.iscram.org/show.php?record=432

Abstract: Disaster Risk Management (DRM) is a multi-dimensional problem complex requiring knowledge and experience from a wide range of disciplines. It also requires a methodology which can collate and organize this knowledge in an effective, transparent manner. Towards this end, seven specialists from the social, natural and engineering sciences collaborated in a facilitated workshop in order to develop a prototype multi-hazard disaster reduction model. The model, developed with computer-Aided morphological analysis (MA), makes it possible to identify and compare risk reduction strategies, and preparedness and mitigation measures, for different types of hazards. Due to time constraints, the model is neither complete nor accurate-but only represents a proof-of-principle. The workshop was sponsored by the Earthquake Disaster Mitigation Research Center (EDM) in Kobe, in January, 2005.