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 mergency situations. However, this process is often complex and affected by the existence of ncertainty. For this reason, from different areas of science, several methods and systems are developed and refined to reduce...

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Autores principales: Méndez Garabetti, Miguel, Bianchini, Germán, Tardivo, María, Caymes Scutari, Paola, Gil Costa, Verónica
Formato: Artículo acceptedVersion
Lenguaje:Inglés
Inglés
Publicado: 2023
Materias:
Hybrid Metaheuristics, Differential Evolution, Evolutionary Algorithms, Fire Prediction, Uncertainty Reduction
Acceso en línea:http://hdl.handle.net/20.500.12272/7956
Aporte de:
RIA - Repositorio Institucional Abierto (UTN) de Universidad Tecnológica Nacional
id I68-R174-20.500.12272-7956
record_format dspace
spelling I68-R174-20.500.12272-79562023-06-06T15:02:10Z Hybrid-Parallel Uncertainty Reduction Method Applied to Forest Fire Spread Prediction Méndez Garabetti, Miguel Bianchini, Germán Tardivo, María Caymes Scutari, Paola Gil Costa, Verónica Hybrid Metaheuristics, Differential Evolution, Evolutionary Algorithms, Fire Prediction, Uncertainty Reduction Fire behavior prediction can be a fundamental tool to reduce losses and damages in mergency situations. However, this process is often complex and affected by the existence of ncertainty. 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 Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina Peer Reviewed 2023-06-06T15:02:10Z 2023-06-06T15:02:10Z 2017-04-01 info:eu-repo/semantics/article acceptedVersion Journal of Computer Science & Technology 1666-6046 http://hdl.handle.net/20.500.12272/7956 eng eng openAccess http://creativecommons.org/publicdomain/zero/1.0/ CC0 1.0 Universal Facultad Regional Mendoza. Universidad Tecnológica Nacional Atribución pdf Journal of Computer Science & Technology (JCS&T) 17(1), 12-19. (2017)
institution Universidad Tecnológica Nacional
institution_str I-68
repository_str R-174
collection RIA - Repositorio Institucional Abierto (UTN)
language Inglés
Inglés
topic Hybrid Metaheuristics, Differential Evolution, Evolutionary Algorithms, Fire Prediction, Uncertainty Reduction
spellingShingle Hybrid Metaheuristics, Differential Evolution, Evolutionary Algorithms, Fire Prediction, Uncertainty Reduction
Méndez Garabetti, Miguel
Bianchini, Germán
Tardivo, María
Caymes Scutari, Paola
Gil Costa, Verónica
Hybrid-Parallel Uncertainty Reduction Method Applied to Forest Fire Spread Prediction
topic_facet Hybrid Metaheuristics, Differential Evolution, Evolutionary Algorithms, Fire Prediction, Uncertainty Reduction
description Fire behavior prediction can be a fundamental tool to reduce losses and damages in mergency situations. However, this process is often complex and affected by the existence of ncertainty. 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
format Artículo
acceptedVersion
author Méndez Garabetti, Miguel
Bianchini, Germán
Tardivo, María
Caymes Scutari, Paola
Gil Costa, Verónica
author_facet Méndez Garabetti, Miguel
Bianchini, Germán
Tardivo, María
Caymes Scutari, Paola
Gil Costa, Verónica
author_sort Méndez Garabetti, Miguel
title Hybrid-Parallel Uncertainty Reduction Method Applied to Forest Fire Spread Prediction
title_short Hybrid-Parallel Uncertainty Reduction Method Applied to Forest Fire Spread Prediction
title_full Hybrid-Parallel Uncertainty Reduction Method Applied to Forest Fire Spread Prediction
title_fullStr Hybrid-Parallel Uncertainty Reduction Method Applied to Forest Fire Spread Prediction
title_full_unstemmed Hybrid-Parallel Uncertainty Reduction Method Applied to Forest Fire Spread Prediction
title_sort hybrid-parallel uncertainty reduction method applied to forest fire spread prediction
publishDate 2023
url http://hdl.handle.net/20.500.12272/7956
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