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Wildfire Susceptibility Maps Flexible Querying and Answering

  • Paolo Arcaini
  • Gloria Bordogna
  • Simone Sterlacchini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8132)

Abstract

Forecasting natural disasters, as wildfires or floods, is a mandatory activity to reduce the level of risk and damage to people, properties and infrastructures. Since estimating real-time the susceptibility to a given phenomenon is computationally onerous, susceptibility maps are usually pre-computed. So, techniques are needed to efficiently query such maps, in order to retrieve the most plausible scenario for the current situation. We propose a flexible querying and answering framework by which the operator, in charge of managing an ongoing disaster, can retrieve the list of susceptibility maps in decreasing order of satisfaction with respect to the query conditions. The operator can also describe trends of the conditions that are related with environmental parameters, assessing what happens if a dynamic parameter is increasing or decreasing in value.

Keywords

Similarity Index Dynamic Parameter Disaster Risk Reduction Soft Constraint Current Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Paolo Arcaini
    • 1
  • Gloria Bordogna
    • 1
  • Simone Sterlacchini
    • 1
  1. 1.Institute for the Study of the Dynamics of Environmental ProcessesCNR – IDPA – National Research Council of ItalyItaly

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