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

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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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Detalles Bibliográficos
Autores principales: Méndez Garabetti, Miguel, BIanchini, Germán, Tardivo, María Laura, Caymes Scutari, Paola, Gil Costa, Graciela Verónica
Formato: Articulo
Lenguaje:Inglés
Publicado: 2017
Materias:
Ciencias Informáticas
hybrid metaheuristics
differential evolution
evolutionary algorithms
fire prediction
uncertainty reduction
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/59977
http://journal.info.unlp.edu.ar/wp-content/uploads/2017/05/JCST-44-Paper-2.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
Descripción
Sumario: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.

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