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

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
Main Authors: Miguel Méndez Garabetti, Germán BIanchini, María Laura Tardivo, Paola Caymes Scutari, Graciela Verónica Gil Costa
Format: Article
Language:English
Published: Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata 2017-04-01
Series:Journal of Computer Science and Technology
Subjects:
hybrid metaheuristics
differential evolution
evolutionary algorithms
fire prediction
uncertainty reduction
Online Access:https://journal.info.unlp.edu.ar/JCST/article/view/455
Description
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

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