ESS-IM applied to Forest Fire Spread Prediction: parameters Tuning for a Heterogeneous Configuration

Autores
Méndez Garabetti, Miguel; Bianchini, Germán; Caymes Scutari, Paola; Tardivo, M
Año de publicación
2016
Idioma
español castellano
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
Forest fires are a critical natural hazard in many regions of the World. For this reason, the prediction of this kind of phenomenon is considered a very important task that involves a high degree of complexity and precision. The ability to predict the forest fire behaviour constitutes an important tool for managers, helping to improve the effectiveness of fire prevention, detection and firefighting resources allocation. For this reason, prediction methods should be configured to operate as efficiently as possible. In this paper, a calibration study of EvolutionaryStatistical System with Island Model’s evolutionary parameters is presented (ESS-IM). ESS-IM is a general-parallel uncertainty reduction method applied to the forest fires spread prediction. Index Terms—forest fire spread prediction, parallel evolutionary algorithms, parameters tuning, high performance computing.
Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina
Peer Reviewed
Materia
Forest fire, Spread prediction, Parallel evolutionary algorithms, Parameters tuning, High performance, Computing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2023-06-12T14:00:25Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/8036
Acceder
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spelling ESS-IM applied to Forest Fire Spread Prediction: parameters Tuning for a Heterogeneous ConfigurationMéndez Garabetti, MiguelBianchini, GermánCaymes Scutari, PaolaTardivo, MForest fire, Spread prediction, Parallel evolutionary algorithms, Parameters tuning, High performance, ComputingForest fires are a critical natural hazard in many regions of the World. For this reason, the prediction of this kind of phenomenon is considered a very important task that involves a high degree of complexity and precision. The ability to predict the forest fire behaviour constitutes an important tool for managers, helping to improve the effectiveness of fire prevention, detection and firefighting resources allocation. For this reason, prediction methods should be configured to operate as efficiently as possible. In this paper, a calibration study of EvolutionaryStatistical System with Island Model’s evolutionary parameters is presented (ESS-IM). ESS-IM is a general-parallel uncertainty reduction method applied to the forest fires spread prediction. Index Terms—forest fire spread prediction, parallel evolutionary algorithms, parameters tuning, high performance computing.Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; ArgentinaPeer Reviewed2023-06-12T14:00:25Z2023-06-12T14:00:25Z2016-10-14info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfhttp://hdl.handle.net/20.500.12272/803610.1109/SCCC.2016.7836007spaPID3939info:eu-repo/semantics/openAccess2023-06-12T14:00:25Zhttp://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 UniversalUniversidad Tecnológica Nacional. Facultad Regional MendozaAtribuciónreponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2026-09-24T12:44:32Zoai:ria.utn.edu.ar:20.500.12272/8036instacron: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 12:44:32.94Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv ESS-IM applied to Forest Fire Spread Prediction: parameters Tuning for a Heterogeneous Configuration
title ESS-IM applied to Forest Fire Spread Prediction: parameters Tuning for a Heterogeneous Configuration
spellingShingle ESS-IM applied to Forest Fire Spread Prediction: parameters Tuning for a Heterogeneous Configuration
Méndez Garabetti, Miguel
Forest fire, Spread prediction, Parallel evolutionary algorithms, Parameters tuning, High performance, Computing
title_short ESS-IM applied to Forest Fire Spread Prediction: parameters Tuning for a Heterogeneous Configuration
title_full ESS-IM applied to Forest Fire Spread Prediction: parameters Tuning for a Heterogeneous Configuration
title_fullStr ESS-IM applied to Forest Fire Spread Prediction: parameters Tuning for a Heterogeneous Configuration
title_full_unstemmed ESS-IM applied to Forest Fire Spread Prediction: parameters Tuning for a Heterogeneous Configuration
title_sort ESS-IM applied to Forest Fire Spread Prediction: parameters Tuning for a Heterogeneous Configuration
dc.creator.none.fl_str_mv Méndez Garabetti, Miguel
Bianchini, Germán
Caymes Scutari, Paola
Tardivo, M
author Méndez Garabetti, Miguel
author_facet Méndez Garabetti, Miguel
Bianchini, Germán
Caymes Scutari, Paola
Tardivo, M
author_role author
author2 Bianchini, Germán
Caymes Scutari, Paola
Tardivo, M
author2_role author
author
author
dc.subject.none.fl_str_mv Forest fire, Spread prediction, Parallel evolutionary algorithms, Parameters tuning, High performance, Computing
topic Forest fire, Spread prediction, Parallel evolutionary algorithms, Parameters tuning, High performance, Computing
dc.description.none.fl_txt_mv Forest fires are a critical natural hazard in many regions of the World. For this reason, the prediction of this kind of phenomenon is considered a very important task that involves a high degree of complexity and precision. The ability to predict the forest fire behaviour constitutes an important tool for managers, helping to improve the effectiveness of fire prevention, detection and firefighting resources allocation. For this reason, prediction methods should be configured to operate as efficiently as possible. In this paper, a calibration study of EvolutionaryStatistical System with Island Model’s evolutionary parameters is presented (ESS-IM). ESS-IM is a general-parallel uncertainty reduction method applied to the forest fires spread prediction. Index Terms—forest fire spread prediction, parallel evolutionary algorithms, parameters tuning, high performance computing.
Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina
Peer Reviewed
description Forest fires are a critical natural hazard in many regions of the World. For this reason, the prediction of this kind of phenomenon is considered a very important task that involves a high degree of complexity and precision. The ability to predict the forest fire behaviour constitutes an important tool for managers, helping to improve the effectiveness of fire prevention, detection and firefighting resources allocation. For this reason, prediction methods should be configured to operate as efficiently as possible. In this paper, a calibration study of EvolutionaryStatistical System with Island Model’s evolutionary parameters is presented (ESS-IM). ESS-IM is a general-parallel uncertainty reduction method applied to the forest fires spread prediction. Index Terms—forest fire spread prediction, parallel evolutionary algorithms, parameters tuning, high performance computing.
publishDate 2016
dc.date.none.fl_str_mv 2016-10-14
2023-06-12T14:00:25Z
2023-06-12T14:00:25Z
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 http://hdl.handle.net/20.500.12272/8036
10.1109/SCCC.2016.7836007
url http://hdl.handle.net/20.500.12272/8036
identifier_str_mv 10.1109/SCCC.2016.7836007
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv PID3939
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2023-06-12T14:00:25Z
http://creativecommons.org/publicdomain/zero/1.0/
CC0 1.0 Universal
Universidad Tecnológica Nacional. Facultad Regional Mendoza
Atribución
eu_rights_str_mv openAccess
rights_invalid_str_mv 2023-06-12T14:00:25Z
http://creativecommons.org/publicdomain/zero/1.0/
CC0 1.0 Universal
Universidad Tecnológica Nacional. Facultad Regional Mendoza
Atribución
dc.format.none.fl_str_mv pdf
application/pdf
dc.source.none.fl_str_mv 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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