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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/59681
Ver los metadatos del registro completo
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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 Técnicas2025-09-03T09:43:35Zoai: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:34982025-09-03 09:43:36.083CONICET 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 |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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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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13.13397 |