Abstract
In order to effectively plan both preparedness and response to emergency situations it is necessary to access and analyse timely information on plausible scenarios of occurrence of ongoing events. Scenario maps representing the estimated susceptibility, hazard or risk of occurrence of an event on a territory are hardly generated real time. In fact the application of physical or statistical models using environmental parameters representing current dynamic conditions is time consuming on low cost hardware equipment. To cope with this practical issue we propose an off line generation of scenario maps under diversified environmental dynamic parameters, and a geo-Spatial Data Infrastructure (SDI) to allow people in charge of emergency preparedness and response activities to flexibly discover, retrieve, fuse and visualize the most plausible scenarios that may happen given some ongoing or forecasted dynamic conditions influencing the event. The novelty described in this chapter is related with both the ability to interpret flexible queries in order to retrieve risk scenario maps that are related to the current situation and to show the most plausible worst and best scenarios that may occur in each elementary area of the territory. Although, the SDI proposal has been conceived and designed to support the management of distinct natural and man-made risks, in the proof of concept prototypal implementation the scenarios maps target wild fire events.
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Aknowledgements
This work has been carried out within the project SISTEMATI—Strumenti Informatici per lo Studio e il Trattamento di Emergenze Ambientali, Tecnologiche e Infrastrutturali and SIMULATOR—Sistema Modulare per la prevenzione dei rischi. Both projects have been funded by Regione Lombardia.
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Bordogna, G. et al. (2017). A Geo-Spatial Data Infrastructure for Flexible Discovery, Retrieval and Fusion of Scenario Maps in Preparedness of Emergency. In: Kacprzyk, J., Filev, D., Beliakov, G. (eds) Granular, Soft and Fuzzy Approaches for Intelligent Systems. Studies in Fuzziness and Soft Computing, vol 344. Springer, Cham. https://doi.org/10.1007/978-3-319-40314-4_9
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DOI: https://doi.org/10.1007/978-3-319-40314-4_9
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