Evolutionary-Statistical System: A parallel method for improving forest fire spread prediction

Autores
Bianchini, German; Caymes Scutari, Paola Guadalupe; Méndez, Miguel Ángel
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Fighting fires is a very risky job, where loss of life is a real possibility. Proper training is essential. Several firemen academies offer courses and programs whose goal is to enhance the ability of fire and emergency services to deal more effectively with fire. Among the tools that can be found in the training process are fire simulators, which are used both for training and for the prediction of forest fires. In many cases, the used simulators are based on models that present a series of limitations related to the need for a large number of input parameters. Moreover, such parameters often have some degree of uncertainty due to the impossibility of measuring all of them in real time. Therefore, they have to be estimated from indirect measurements, which negatively impacts on the output of the model. In this paper we present a method which combines Statistical Analysis with Parallel Evolutionary Algorithms to improve the quality of the model output.
Fil: Bianchini, German. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Mendoza-San Juan; Argentina
Fil: Caymes Scutari, Paola Guadalupe. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Mendoza-San Juan; Argentina
Fil: Méndez, Miguel Ángel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Mendoza-San Juan; Argentina
Materia
Forest Fire Prediction
High Performance Computing
Parallel Evolutionary Algorithm
Parallel Processing
Statistical System
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:11336/59681
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Evolutionary-Statistical System: A parallel method for improving forest fire spread predictionBianchini, GermanCaymes Scutari, Paola GuadalupeMéndez, Miguel ÁngelForest Fire PredictionHigh Performance ComputingParallel Evolutionary AlgorithmParallel ProcessingStatistical Systemhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Fighting fires is a very risky job, where loss of life is a real possibility. Proper training is essential. Several firemen academies offer courses and programs whose goal is to enhance the ability of fire and emergency services to deal more effectively with fire. Among the tools that can be found in the training process are fire simulators, which are used both for training and for the prediction of forest fires. In many cases, the used simulators are based on models that present a series of limitations related to the need for a large number of input parameters. Moreover, such parameters often have some degree of uncertainty due to the impossibility of measuring all of them in real time. Therefore, they have to be estimated from indirect measurements, which negatively impacts on the output of the model. In this paper we present a method which combines Statistical Analysis with Parallel Evolutionary Algorithms to improve the quality of the model output.Fil: Bianchini, German. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Mendoza-San Juan; ArgentinaFil: Caymes Scutari, Paola Guadalupe. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Mendoza-San Juan; ArgentinaFil: Méndez, Miguel Ángel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Mendoza-San Juan; ArgentinaElsevier2015-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/59681Bianchini, German; Caymes Scutari, Paola Guadalupe; Méndez, Miguel Ángel; Evolutionary-Statistical System: A parallel method for improving forest fire spread prediction; Elsevier; Journal of Computational Science; 6; 1; 1-2015; 58-661877-7503CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jocs.2014.12.001info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1877750314001628info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:32:54Zoai:ri.conicet.gov.ar:11336/59681instacron: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 09:32:55.1CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Evolutionary-Statistical System: A parallel method for improving forest fire spread prediction
title Evolutionary-Statistical System: A parallel method for improving forest fire spread prediction
spellingShingle Evolutionary-Statistical System: A parallel method for improving forest fire spread prediction
Bianchini, German
Forest Fire Prediction
High Performance Computing
Parallel Evolutionary Algorithm
Parallel Processing
Statistical System
title_short Evolutionary-Statistical System: A parallel method for improving forest fire spread prediction
title_full Evolutionary-Statistical System: A parallel method for improving forest fire spread prediction
title_fullStr Evolutionary-Statistical System: A parallel method for improving forest fire spread prediction
title_full_unstemmed Evolutionary-Statistical System: A parallel method for improving forest fire spread prediction
title_sort Evolutionary-Statistical System: A parallel method for improving forest fire spread prediction
dc.creator.none.fl_str_mv Bianchini, German
Caymes Scutari, Paola Guadalupe
Méndez, Miguel Ángel
author Bianchini, German
author_facet Bianchini, German
Caymes Scutari, Paola Guadalupe
Méndez, Miguel Ángel
author_role author
author2 Caymes Scutari, Paola Guadalupe
Méndez, Miguel Ángel
author2_role author
author
dc.subject.none.fl_str_mv Forest Fire Prediction
High Performance Computing
Parallel Evolutionary Algorithm
Parallel Processing
Statistical System
topic Forest Fire Prediction
High Performance Computing
Parallel Evolutionary Algorithm
Parallel Processing
Statistical System
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Fighting fires is a very risky job, where loss of life is a real possibility. Proper training is essential. Several firemen academies offer courses and programs whose goal is to enhance the ability of fire and emergency services to deal more effectively with fire. Among the tools that can be found in the training process are fire simulators, which are used both for training and for the prediction of forest fires. In many cases, the used simulators are based on models that present a series of limitations related to the need for a large number of input parameters. Moreover, such parameters often have some degree of uncertainty due to the impossibility of measuring all of them in real time. Therefore, they have to be estimated from indirect measurements, which negatively impacts on the output of the model. In this paper we present a method which combines Statistical Analysis with Parallel Evolutionary Algorithms to improve the quality of the model output.
Fil: Bianchini, German. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Mendoza-San Juan; Argentina
Fil: Caymes Scutari, Paola Guadalupe. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Mendoza-San Juan; Argentina
Fil: Méndez, Miguel Ángel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Mendoza-San Juan; Argentina
description Fighting fires is a very risky job, where loss of life is a real possibility. Proper training is essential. Several firemen academies offer courses and programs whose goal is to enhance the ability of fire and emergency services to deal more effectively with fire. Among the tools that can be found in the training process are fire simulators, which are used both for training and for the prediction of forest fires. In many cases, the used simulators are based on models that present a series of limitations related to the need for a large number of input parameters. Moreover, such parameters often have some degree of uncertainty due to the impossibility of measuring all of them in real time. Therefore, they have to be estimated from indirect measurements, which negatively impacts on the output of the model. In this paper we present a method which combines Statistical Analysis with Parallel Evolutionary Algorithms to improve the quality of the model output.
publishDate 2015
dc.date.none.fl_str_mv 2015-01
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/11336/59681
Bianchini, German; Caymes Scutari, Paola Guadalupe; Méndez, Miguel Ángel; Evolutionary-Statistical System: A parallel method for improving forest fire spread prediction; Elsevier; Journal of Computational Science; 6; 1; 1-2015; 58-66
1877-7503
CONICET Digital
CONICET
url http://hdl.handle.net/11336/59681
identifier_str_mv Bianchini, German; Caymes Scutari, Paola Guadalupe; Méndez, Miguel Ángel; Evolutionary-Statistical System: A parallel method for improving forest fire spread prediction; Elsevier; Journal of Computational Science; 6; 1; 1-2015; 58-66
1877-7503
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jocs.2014.12.001
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1877750314001628
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
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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
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score 13.069144

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