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

Autores
Méndez Garabetti, Miguel; BIanchini, Germán; Tardivo, María Laura; Caymes Scutari, Paola; Gil Costa, Graciela Verónica
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Facultad de Informática
Materia
Ciencias Informáticas
hybrid metaheuristics
differential evolution
evolutionary algorithms
fire prediction
uncertainty reduction
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/59977
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/59977
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Hybrid-Parallel Uncertainty Reduction Method Applied to Forest Fire Spread PredictionMéndez Garabetti, MiguelBIanchini, GermánTardivo, María LauraCaymes Scutari, PaolaGil Costa, Graciela VerónicaCiencias Informáticashybrid metaheuristicsdifferential evolutionevolutionary algorithmsfire predictionuncertainty reductionFire 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.Facultad de Informática2017-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf12-19http://sedici.unlp.edu.ar/handle/10915/59977enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/2017/05/JCST-44-Paper-2.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/Creative Commons Attribution 3.0 Unported (CC BY 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-10T12:10:28Zoai:sedici.unlp.edu.ar:10915/59977Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-10 12:10:29.046SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Hybrid-Parallel Uncertainty Reduction Method Applied to Forest Fire Spread Prediction
title Hybrid-Parallel Uncertainty Reduction Method Applied to Forest Fire Spread Prediction
spellingShingle Hybrid-Parallel Uncertainty Reduction Method Applied to Forest Fire Spread Prediction
Méndez Garabetti, Miguel
Ciencias Informáticas
hybrid metaheuristics
differential evolution
evolutionary algorithms
fire prediction
uncertainty reduction
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
dc.creator.none.fl_str_mv Méndez Garabetti, Miguel
BIanchini, Germán
Tardivo, María Laura
Caymes Scutari, Paola
Gil Costa, Graciela Verónica
author Méndez Garabetti, Miguel
author_facet Méndez Garabetti, Miguel
BIanchini, Germán
Tardivo, María Laura
Caymes Scutari, Paola
Gil Costa, Graciela Verónica
author_role author
author2 BIanchini, Germán
Tardivo, María Laura
Caymes Scutari, Paola
Gil Costa, Graciela Verónica
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
hybrid metaheuristics
differential evolution
evolutionary algorithms
fire prediction
uncertainty reduction
topic Ciencias Informáticas
hybrid metaheuristics
differential evolution
evolutionary algorithms
fire prediction
uncertainty reduction
dc.description.none.fl_txt_mv 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.
Facultad de Informática
description 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.
publishDate 2017
dc.date.none.fl_str_mv 2017-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/59977
url http://sedici.unlp.edu.ar/handle/10915/59977
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/2017/05/JCST-44-Paper-2.pdf
info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/3.0/
Creative Commons Attribution 3.0 Unported (CC BY 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/
Creative Commons Attribution 3.0 Unported (CC BY 3.0)
dc.format.none.fl_str_mv application/pdf
12-19
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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