Hybrid-Parallel Uncertainty Reduction Method Applied to Forest Fire Spread Prediction

Fire behavior prediction can be a fundamental tool to reduce losses and damages in emergency situations. However, this process is often complex and affected by the existence of uncertainty. For this reason, from different areas of science, several methods and systems are developed and refined to red...

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Bibliographic Details
Published in:Journal of computer science and technology (La Plata) 2017-04, Vol.17 (1), p.12-19
Main Authors: Miguel Méndez Garabetti, BIanchini, Germán, Tardivo, María Laura, Scutari, Paola Caymes, Gil Costa, Graciela Verónica
Format: Article
Language:eng ; spa
Subjects:
differential evolution
Evolutionary algorithms
Evolutionary computation
fire prediction
Forest fires
Heuristic methods
hybrid metaheuristics
Hybrid systems
Reduction
Uncertainty
uncertainty reduction
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Summary:Fire behavior prediction can be a fundamental tool to reduce losses and damages in emergency situations. However, this process is often complex and affected by the existence of uncertainty. For this reason, from different areas of science, several methods and systems are developed and refined to reduce the effects of uncertainty In this paper we present the Hybrid Evolutionary-Statistical System with Island Model (HESS-IM). It is a hybrid uncertainty reduction method applied to forest fire spread prediction that combines the advantages of two evolutionary population metaheuristics: Evolutionary Algorithms and Differential Evolution. We evaluate the HESS-IM with three controlled fires scenarios, and we obtained favorable results compared to the previous methods in the literature.
ISSN:1666-6046
1666-6038