Hybrid-parallel uncertainty reduction method applied to forest fire spread prediction

Autores
Mendez Garabetti, Miguel; Bianchini, German; Tardivo, María Laura; Caymes Scutari, Paola Guadalupe; 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.
Fil: Mendez Garabetti, Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio de Investigación en Cómputo Paralelo/Distribuido; Argentina
Fil: Bianchini, German. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio de Investigación en Cómputo Paralelo/Distribuido; Argentina
Fil: Tardivo, María Laura. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio de Investigación en Cómputo Paralelo/Distribuido; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Caymes Scutari, Paola Guadalupe. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio de Investigación en Cómputo Paralelo/Distribuido; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físico-Químicas y Naturales; Argentina
Fil: Gil Costa, Graciela Verónica. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físico-Químicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
HYBRID METAHEURISTICS
DIFFERENTIAL EVOLUTION
EVOLUTIONARY ALGORITHMS
FIRE PREDICTION
UNCERTAINTY REDUCTION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/69844
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Hybrid-parallel uncertainty reduction method applied to forest fire spread predictionMendez Garabetti, MiguelBianchini, GermanTardivo, María LauraCaymes Scutari, Paola GuadalupeGil Costa, Graciela VerónicaHYBRID METAHEURISTICSDIFFERENTIAL EVOLUTIONEVOLUTIONARY ALGORITHMSFIRE PREDICTIONUNCERTAINTY REDUCTIONhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Fire 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.Fil: Mendez Garabetti, Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio de Investigación en Cómputo Paralelo/Distribuido; ArgentinaFil: Bianchini, German. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio de Investigación en Cómputo Paralelo/Distribuido; ArgentinaFil: Tardivo, María Laura. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio de Investigación en Cómputo Paralelo/Distribuido; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Caymes Scutari, Paola Guadalupe. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio de Investigación en Cómputo Paralelo/Distribuido; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físico-Químicas y Naturales; ArgentinaFil: Gil Costa, Graciela Verónica. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físico-Químicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaIbero-American Science and Technology Education Consortium2017-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/69844Mendez Garabetti, Miguel; Bianchini, German; Tardivo, María Laura; Caymes Scutari, Paola Guadalupe; Gil Costa, Graciela Verónica; Hybrid-parallel uncertainty reduction method applied to forest fire spread prediction; Ibero-American Science and Technology Education Consortium; Journal of Computer Science & Technology; 17; 1; 4-2017; 12-191666-6046CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://sedici.unlp.edu.ar/handle/10915/59977info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:23:23Zoai:ri.conicet.gov.ar:11336/69844instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:23:23.32CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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
Mendez 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 Mendez Garabetti, Miguel
Bianchini, German
Tardivo, María Laura
Caymes Scutari, Paola Guadalupe
Gil Costa, Graciela Verónica
author Mendez Garabetti, Miguel
author_facet Mendez Garabetti, Miguel
Bianchini, German
Tardivo, María Laura
Caymes Scutari, Paola Guadalupe
Gil Costa, Graciela Verónica
author_role author
author2 Bianchini, German
Tardivo, María Laura
Caymes Scutari, Paola Guadalupe
Gil Costa, Graciela 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
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
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.
Fil: Mendez Garabetti, Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio de Investigación en Cómputo Paralelo/Distribuido; Argentina
Fil: Bianchini, German. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio de Investigación en Cómputo Paralelo/Distribuido; Argentina
Fil: Tardivo, María Laura. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio de Investigación en Cómputo Paralelo/Distribuido; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Caymes Scutari, Paola Guadalupe. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio de Investigación en Cómputo Paralelo/Distribuido; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físico-Químicas y Naturales; Argentina
Fil: Gil Costa, Graciela Verónica. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físico-Químicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
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
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://hdl.handle.net/11336/69844
Mendez Garabetti, Miguel; Bianchini, German; Tardivo, María Laura; Caymes Scutari, Paola Guadalupe; Gil Costa, Graciela Verónica; Hybrid-parallel uncertainty reduction method applied to forest fire spread prediction; Ibero-American Science and Technology Education Consortium; Journal of Computer Science & Technology; 17; 1; 4-2017; 12-19
1666-6046
CONICET Digital
CONICET
url http://hdl.handle.net/11336/69844
identifier_str_mv Mendez Garabetti, Miguel; Bianchini, German; Tardivo, María Laura; Caymes Scutari, Paola Guadalupe; Gil Costa, Graciela Verónica; Hybrid-parallel uncertainty reduction method applied to forest fire spread prediction; Ibero-American Science and Technology Education Consortium; Journal of Computer Science & Technology; 17; 1; 4-2017; 12-19
1666-6046
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://sedici.unlp.edu.ar/handle/10915/59977
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Ibero-American Science and Technology Education Consortium
publisher.none.fl_str_mv Ibero-American Science and Technology Education Consortium
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844614228337491968
score 13.069144

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