Comparative analysis of performance and quality of prediction between ESS and ESS-IM

Autores
Méndez, Miguel Ángel; Bianchini, German; Tardivo, María Laura; Caymes Scutari, Paola Guadalupe
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Wildfires cause major damage and losses around the world. Such damages range from human and economical losses to environmental ones. Therefore, having models to predict their behavior can be a key element in the process of firefighting. In this paper, we present a comparative study between two methods we have developed. Both methods use Statistical Analysis, Parallel Evolutionary Algorithms and High Performance Computing, respectively named: Evolutionary-Statistical System (ESS) and Evolutionary-Statistical System with Island Model (ESS-IM). In this study, we have compared these two methods in terms of quality of prediction and performance in the parallel environment.
Fil: Méndez, Miguel Ángel. 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. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio de Investigación en Cómputo Paralelo/Distribuido; Argentina. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Departamento de Computación; Argentina
Fil: Caymes Scutari, Paola Guadalupe. 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
Materia
EVOLUTIONARY ALGORITHMS
HIGH PERFORMANCE COMPUTING
SPEED-UP
WILDFIRES PREDICTION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/178557
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Comparative analysis of performance and quality of prediction between ESS and ESS-IMMéndez, Miguel ÁngelBianchini, GermanTardivo, María LauraCaymes Scutari, Paola GuadalupeEVOLUTIONARY ALGORITHMSHIGH PERFORMANCE COMPUTINGSPEED-UPWILDFIRES PREDICTIONhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Wildfires cause major damage and losses around the world. Such damages range from human and economical losses to environmental ones. Therefore, having models to predict their behavior can be a key element in the process of firefighting. In this paper, we present a comparative study between two methods we have developed. Both methods use Statistical Analysis, Parallel Evolutionary Algorithms and High Performance Computing, respectively named: Evolutionary-Statistical System (ESS) and Evolutionary-Statistical System with Island Model (ESS-IM). In this study, we have compared these two methods in terms of quality of prediction and performance in the parallel environment.Fil: Méndez, Miguel Ángel. 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. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio de Investigación en Cómputo Paralelo/Distribuido; Argentina. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Departamento de Computación; ArgentinaFil: Caymes Scutari, Paola Guadalupe. 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; ArgentinaElsevier2015-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/178557Méndez, Miguel Ángel; Bianchini, German; Tardivo, María Laura; Caymes Scutari, Paola Guadalupe; Comparative analysis of performance and quality of prediction between ESS and ESS-IM; Elsevier; Electronic Notes in Theoretical Computer Science; 314; 6-2015; 45-601571-0661CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1571066115000274info:eu-repo/semantics/altIdentifier/doi/10.1016/j.entcs.2015.05.004info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:53:56Zoai:ri.conicet.gov.ar:11336/178557instacron: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 09:53:57.011CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Comparative analysis of performance and quality of prediction between ESS and ESS-IM
title Comparative analysis of performance and quality of prediction between ESS and ESS-IM
spellingShingle Comparative analysis of performance and quality of prediction between ESS and ESS-IM
Méndez, Miguel Ángel
EVOLUTIONARY ALGORITHMS
HIGH PERFORMANCE COMPUTING
SPEED-UP
WILDFIRES PREDICTION
title_short Comparative analysis of performance and quality of prediction between ESS and ESS-IM
title_full Comparative analysis of performance and quality of prediction between ESS and ESS-IM
title_fullStr Comparative analysis of performance and quality of prediction between ESS and ESS-IM
title_full_unstemmed Comparative analysis of performance and quality of prediction between ESS and ESS-IM
title_sort Comparative analysis of performance and quality of prediction between ESS and ESS-IM
dc.creator.none.fl_str_mv Méndez, Miguel Ángel
Bianchini, German
Tardivo, María Laura
Caymes Scutari, Paola Guadalupe
author Méndez, Miguel Ángel
author_facet Méndez, Miguel Ángel
Bianchini, German
Tardivo, María Laura
Caymes Scutari, Paola Guadalupe
author_role author
author2 Bianchini, German
Tardivo, María Laura
Caymes Scutari, Paola Guadalupe
author2_role author
author
author
dc.subject.none.fl_str_mv EVOLUTIONARY ALGORITHMS
HIGH PERFORMANCE COMPUTING
SPEED-UP
WILDFIRES PREDICTION
topic EVOLUTIONARY ALGORITHMS
HIGH PERFORMANCE COMPUTING
SPEED-UP
WILDFIRES PREDICTION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Wildfires cause major damage and losses around the world. Such damages range from human and economical losses to environmental ones. Therefore, having models to predict their behavior can be a key element in the process of firefighting. In this paper, we present a comparative study between two methods we have developed. Both methods use Statistical Analysis, Parallel Evolutionary Algorithms and High Performance Computing, respectively named: Evolutionary-Statistical System (ESS) and Evolutionary-Statistical System with Island Model (ESS-IM). In this study, we have compared these two methods in terms of quality of prediction and performance in the parallel environment.
Fil: Méndez, Miguel Ángel. 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. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Tecnológica Nacional. Facultad Regional Mendoza. Departamento de Sistemas de Información. Laboratorio de Investigación en Cómputo Paralelo/Distribuido; Argentina. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Departamento de Computación; Argentina
Fil: Caymes Scutari, Paola Guadalupe. 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
description Wildfires cause major damage and losses around the world. Such damages range from human and economical losses to environmental ones. Therefore, having models to predict their behavior can be a key element in the process of firefighting. In this paper, we present a comparative study between two methods we have developed. Both methods use Statistical Analysis, Parallel Evolutionary Algorithms and High Performance Computing, respectively named: Evolutionary-Statistical System (ESS) and Evolutionary-Statistical System with Island Model (ESS-IM). In this study, we have compared these two methods in terms of quality of prediction and performance in the parallel environment.
publishDate 2015
dc.date.none.fl_str_mv 2015-06
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/11336/178557
Méndez, Miguel Ángel; Bianchini, German; Tardivo, María Laura; Caymes Scutari, Paola Guadalupe; Comparative analysis of performance and quality of prediction between ESS and ESS-IM; Elsevier; Electronic Notes in Theoretical Computer Science; 314; 6-2015; 45-60
1571-0661
CONICET Digital
CONICET
url http://hdl.handle.net/11336/178557
identifier_str_mv Méndez, Miguel Ángel; Bianchini, German; Tardivo, María Laura; Caymes Scutari, Paola Guadalupe; Comparative analysis of performance and quality of prediction between ESS and ESS-IM; Elsevier; Electronic Notes in Theoretical Computer Science; 314; 6-2015; 45-60
1571-0661
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1571066115000274
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.entcs.2015.05.004
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
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 13.070432

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