Dynamic Data-Driven Genetic Algorithm for forest fire spread prediction

Mónica Denham, Kerstin Wendt, Germán Bianchini, Ana Cortés, Tomàs Margalef

Research output: Contribution to journalArticleResearchpeer-review

40 Citations (Scopus)

Abstract

This work represents the first step towards a Dynamic Data-Driven Application System (DDDAS) for wildland fire prediction. Our main efforts are focused on taking advantage of the computing power provided by High Performance Computing systems and to propose computational data-driven steering strategies to overcome input data uncertainty. In doing so, prediction quality can be enhanced significantly. On the other hand, these proposals reduce the execution time of the overall prediction process in order to be of use during real-time crisis. In particular, this work describes a Dynamic Data-Driven Genetic Algorithm (DDDGA) used as steering strategy to automatically adjust highly dynamic input data values of forest fire simulators taking into account the underlying propagation model and real fire behaviour. © 2012 Elsevier B.V.
Original languageEnglish
Pages (from-to)398-404
JournalJournal of Computational Science
Volume3
DOIs
Publication statusPublished - 1 Sep 2012

Keywords

  • DDDAS
  • Forest fire
  • Genetic algorithm
  • Prediction quality
  • Simulation

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