A Geo-Spatial Data Infrastructure for Flexible Discovery, Retrieval and Fusion of Scenario Maps in Preparedness of Emergency
Capitolo di libro
Data di Pubblicazione:
2017
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.
Tipologia CRIS:
02.01 Contributo in volume (Capitolo o Saggio)
Keywords:
Susceptibility; Hazard; Risk maps; Geo-Spatial data infrastructure; Database flexible querying; Decision support system
Elenco autori:
Mangioni, Elisabetta; Cappellini, Giacomo; Polyzoni, Chrysanthi; Cugini, Mattia; Sterlacchini, Simone; Bordogna, Gloria
Link alla scheda completa:
Titolo del libro:
Granular, Soft and Fuzzy Approaches for Intelligent Systems
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