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Improved Prediction Methods for Wildfires Using High Performance Computing: A Comparison

  • Germán Bianchini
  • Ana Cortés
  • Tomàs Margalef
  • Emilio Luque
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3991)

Abstract

Recently, dry and hot seasons have seriously increased the risk of forest fire in the Mediterranean area. Wildland simulators, used to predict fire behavior, can give erroneous forecasts due to lack of precision for certain dynamic input parameters. Developing methods to avoid such parameter problems can improve significantly the fire behavior prediction. In this paper, two methods are evaluated, involving statistical and uncertainty schemes. In each one, the number of simulations that must be carried out is enormous and it is necessary to apply high-performance computing techniques to make the methodology feasible. These techniques have been implemented in parallel schemes and tested in Linux cluster using MPI.

Keywords

Wildland Fire Ignition Probability Fire Behavior Prediction Real Spread Forest Fire Spread 
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 2006

Authors and Affiliations

  • Germán Bianchini
    • 1
  • Ana Cortés
    • 1
  • Tomàs Margalef
    • 1
  • Emilio Luque
    • 1
  1. 1.Departament d’Informàtica, E.T.S.EUniversitat Autònoma de BarcelonaBellaterra (Barcelona)Spain

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