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

Autores
Méndez Garabetti, Miguel; Bianchini, Germán; Tardivo, María; Caymes Scutari, Paola; Gil Costa, Verónica
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
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
Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina
Peer Reviewed
Fuente
Journal of Computer Science & Technology (JCS&T) 17(1), 12-19. (2017)
Materia
Hybrid Metaheuristics, Differential Evolution, Evolutionary Algorithms, Fire Prediction, Uncertainty Reduction
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2023-06-06T15:02:10Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/7956
Acceder
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spelling Hybrid-Parallel Uncertainty Reduction Method Applied to Forest Fire Spread PredictionMéndez Garabetti, MiguelBianchini, GermánTardivo, MaríaCaymes Scutari, PaolaGil Costa, VerónicaHybrid Metaheuristics, Differential Evolution, Evolutionary Algorithms, Fire Prediction, Uncertainty ReductionFire 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 literatureFil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; ArgentinaPeer Reviewed2023-06-06T15:02:10Z2023-06-06T15:02:10Z2017-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfJournal of Computer Science & Technology1666-6046http://hdl.handle.net/20.500.12272/7956Journal of Computer Science & Technology (JCS&T) 17(1), 12-19. (2017)reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacionalengenginfo:eu-repo/semantics/openAccess2023-06-06T15:02:10Zhttp://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 UniversalFacultad Regional Mendoza. Universidad Tecnológica NacionalAtribución2026-09-24T12:45:33Zoai:ria.utn.edu.ar:20.500.12272/7956instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:45:34.675Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
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
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
Caymes Scutari, Paola
Gil Costa, Verónica
author Méndez Garabetti, Miguel
author_facet Méndez Garabetti, Miguel
Bianchini, Germán
Tardivo, María
Caymes Scutari, Paola
Gil Costa, Verónica
author_role author
author2 Bianchini, Germán
Tardivo, María
Caymes Scutari, Paola
Gil Costa, Verónica
author2_role author
author
author
author
dc.subject.none.fl_str_mv Hybrid Metaheuristics, Differential Evolution, Evolutionary Algorithms, Fire Prediction, Uncertainty Reduction
topic 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 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
Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina
Peer Reviewed
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
publishDate 2017
dc.date.none.fl_str_mv 2017-04-01
2023-06-06T15:02:10Z
2023-06-06T15:02:10Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv Journal of Computer Science & Technology
1666-6046
http://hdl.handle.net/20.500.12272/7956
identifier_str_mv Journal of Computer Science & Technology
1666-6046
url http://hdl.handle.net/20.500.12272/7956
dc.language.none.fl_str_mv eng
eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2023-06-06T15:02:10Z
http://creativecommons.org/publicdomain/zero/1.0/
CC0 1.0 Universal
Facultad Regional Mendoza. Universidad Tecnológica Nacional
Atribución
eu_rights_str_mv openAccess
rights_invalid_str_mv 2023-06-06T15:02:10Z
http://creativecommons.org/publicdomain/zero/1.0/
CC0 1.0 Universal
Facultad Regional Mendoza. Universidad Tecnológica Nacional
Atribución
dc.format.none.fl_str_mv pdf
application/pdf
dc.source.none.fl_str_mv Journal of Computer Science & Technology (JCS&T) 17(1), 12-19. (2017)
reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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score 13.24418

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