Parallel dynamic data driven genetic algorithm for forest fire prediction

Mónica Denham*, Ana Cortés, Tomás Margalef

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)


Forest fire simulators are a very useful tool for predicting fire behavior. A forest fire simulator needs to be fed with data related to the environment where fire occurs: terrain main features, weather conditions, fuel type, fuel load and fuel moistures, wind conditions, etc. However, it is very difficult to obtain the real values of these parameters during a disaster [1]. The lack of accuracy of the input parameter values adds uncertainty to any prediction method and it usually provokes low quality simulations.


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