Applying a dynamic data driven genetic algorithm to improve forest fire spread prediction

Mónica Denham*, Ana Cortés, Tomàs Margalef, Emilio Luque

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

33 Citations (Scopus)

Abstract

This work represents the first step toward a DDDAS for Wildland Fire Prediction where our main efforts are oriented to take advantage of the computing power provided by High Performance Computing systems to, on the one hand, propose computational data driven steering strategies to overcome input data uncertainty and, on the other hand, to reduce the execution time of the whole prediction process in order to be reliable during real-time crisis. In particular, this work is focused on the description of a Dynamic Data Driven Genetic Algorithm used as steering strategy to automatic adjust certain input data values of forest fire simulators taking into account the underlying propagation model and the real fire behavior.

Original languageAmerican English
Pages (from-to)36-45
Number of pages10
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Issue numberPART 3
DOIs
Publication statusPublished - 2008

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