Parallel dynamic data driven genetic algorithm for forest fire prediction

Mónica Denham; Ana Cortés; Tomás Margalef

doi:10.1007/978-3-642-03770-2_40

Parallel dynamic data driven genetic algorithm for forest fire prediction

Mónica Denham^*, Ana Cortés, Tomás Margalef

^*Corresponding author for this work

Department of Computer Architecture and Operating Systems

Universitat Autònoma de Barcelona (UAB)

Research output: Contribution to journal › Article › Research › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	American English
Pages (from-to)	323-324
Number of pages	2
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DOIs	https://doi.org/10.1007/978-3-642-03770-2_40
Publication status	Published - 2009

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Cite this

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title = "Parallel dynamic data driven genetic algorithm for forest fire prediction",

abstract = "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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AU - Denham, Mónica

AU - Cortés, Ana

AU - Margalef, Tomás

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AB - 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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DO - 10.1007/978-3-642-03770-2_40

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JO - Lecture Notes in Computer Science

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Parallel dynamic data driven genetic algorithm for forest fire prediction

Abstract

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