Optimization for an Uncertainty Reduction Method Applied to Forest Fires Spread Prediction

Autores
Tardivo, María; Caymes Scutari, Paola; Méndez Garabetti, Miguel; Bianchini, Germán
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
Forest fires prediction represents a great computational and mathematical challenge. The complexity lies both in the definition of mathematical models for describing the physical phenomenon and in the impossibility of measuring in real time all the parameters that determine the fire behaviour. ESSIM (Evolutionary Statistical System with Island Model) is an uncertainty reduction method that uses Statistic, High Performance Computing and Evolutionary Strategies in order to guide the search towards better solutions. ESSIM has been implemented with two different search strategies: the method ESSIM-EA uses Evolutionary Algorithms as optimization engine, whilst ESSIM-DE uses the Differential Evolution algorithm. ESSIM-EA has shown to obtain good quality of predictions, while ESSIM-DE obtains better response times. This article presents an alternative to improve the quality of solutions reached by ESSIM-DE, based on the analysis of the relationship between the evolutionary strategy convergence speed and the population distribution at the beginning of each prediction step.
Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina
Peer Reviewed
Fuente
Computer Science 790, 13-23. (2018)
Materia
Forest fires ,Island model, Evolutionary Algorithms, Prediction, Differential Evolution, Parallelism
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2023-06-21T16:38:19Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/8074
Acceder
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network_name_str Repositorio Institucional Abierto (UTN)
spelling Optimization for an Uncertainty Reduction Method Applied to Forest Fires Spread PredictionTardivo, MaríaCaymes Scutari, PaolaMéndez Garabetti, MiguelBianchini, GermánForest fires ,Island model, Evolutionary Algorithms, Prediction, Differential Evolution, ParallelismForest fires prediction represents a great computational and mathematical challenge. The complexity lies both in the definition of mathematical models for describing the physical phenomenon and in the impossibility of measuring in real time all the parameters that determine the fire behaviour. ESSIM (Evolutionary Statistical System with Island Model) is an uncertainty reduction method that uses Statistic, High Performance Computing and Evolutionary Strategies in order to guide the search towards better solutions. ESSIM has been implemented with two different search strategies: the method ESSIM-EA uses Evolutionary Algorithms as optimization engine, whilst ESSIM-DE uses the Differential Evolution algorithm. ESSIM-EA has shown to obtain good quality of predictions, while ESSIM-DE obtains better response times. This article presents an alternative to improve the quality of solutions reached by ESSIM-DE, based on the analysis of the relationship between the evolutionary strategy convergence speed and the population distribution at the beginning of each prediction step.Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; ArgentinaPeer Reviewed2023-06-21T16:38:19Z2023-06-21T16:38:19Z2018-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfComputer Sciencehttp://hdl.handle.net/20.500.12272/807410.1007/978-3-319-75214-3_2Computer Science 790, 13-23. (2018)reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacionalenginfo:eu-repo/semantics/openAccess2023-06-21T16:38:19Zhttp://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 UniversalUniversidad Tecnológica Nacional. Facultad Regional MendozaAtribución2026-09-24T12:45:09Zoai:ria.utn.edu.ar:20.500.12272/8074instacron: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 12:45:11.002Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Optimization for an Uncertainty Reduction Method Applied to Forest Fires Spread Prediction
title Optimization for an Uncertainty Reduction Method Applied to Forest Fires Spread Prediction
spellingShingle Optimization for an Uncertainty Reduction Method Applied to Forest Fires Spread Prediction
Tardivo, María
Forest fires ,Island model, Evolutionary Algorithms, Prediction, Differential Evolution, Parallelism
title_short Optimization for an Uncertainty Reduction Method Applied to Forest Fires Spread Prediction
title_full Optimization for an Uncertainty Reduction Method Applied to Forest Fires Spread Prediction
title_fullStr Optimization for an Uncertainty Reduction Method Applied to Forest Fires Spread Prediction
title_full_unstemmed Optimization for an Uncertainty Reduction Method Applied to Forest Fires Spread Prediction
title_sort Optimization for an Uncertainty Reduction Method Applied to Forest Fires Spread Prediction
dc.creator.none.fl_str_mv Tardivo, María
Caymes Scutari, Paola
Méndez Garabetti, Miguel
Bianchini, Germán
author Tardivo, María
author_facet Tardivo, María
Caymes Scutari, Paola
Méndez Garabetti, Miguel
Bianchini, Germán
author_role author
author2 Caymes Scutari, Paola
Méndez Garabetti, Miguel
Bianchini, Germán
author2_role author
author
author
dc.subject.none.fl_str_mv Forest fires ,Island model, Evolutionary Algorithms, Prediction, Differential Evolution, Parallelism
topic Forest fires ,Island model, Evolutionary Algorithms, Prediction, Differential Evolution, Parallelism
dc.description.none.fl_txt_mv Forest fires prediction represents a great computational and mathematical challenge. The complexity lies both in the definition of mathematical models for describing the physical phenomenon and in the impossibility of measuring in real time all the parameters that determine the fire behaviour. ESSIM (Evolutionary Statistical System with Island Model) is an uncertainty reduction method that uses Statistic, High Performance Computing and Evolutionary Strategies in order to guide the search towards better solutions. ESSIM has been implemented with two different search strategies: the method ESSIM-EA uses Evolutionary Algorithms as optimization engine, whilst ESSIM-DE uses the Differential Evolution algorithm. ESSIM-EA has shown to obtain good quality of predictions, while ESSIM-DE obtains better response times. This article presents an alternative to improve the quality of solutions reached by ESSIM-DE, based on the analysis of the relationship between the evolutionary strategy convergence speed and the population distribution at the beginning of each prediction step.
Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina
Peer Reviewed
description Forest fires prediction represents a great computational and mathematical challenge. The complexity lies both in the definition of mathematical models for describing the physical phenomenon and in the impossibility of measuring in real time all the parameters that determine the fire behaviour. ESSIM (Evolutionary Statistical System with Island Model) is an uncertainty reduction method that uses Statistic, High Performance Computing and Evolutionary Strategies in order to guide the search towards better solutions. ESSIM has been implemented with two different search strategies: the method ESSIM-EA uses Evolutionary Algorithms as optimization engine, whilst ESSIM-DE uses the Differential Evolution algorithm. ESSIM-EA has shown to obtain good quality of predictions, while ESSIM-DE obtains better response times. This article presents an alternative to improve the quality of solutions reached by ESSIM-DE, based on the analysis of the relationship between the evolutionary strategy convergence speed and the population distribution at the beginning of each prediction step.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-01
2023-06-21T16:38:19Z
2023-06-21T16:38:19Z
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 Computer Science
http://hdl.handle.net/20.500.12272/8074
10.1007/978-3-319-75214-3_2
identifier_str_mv Computer Science
10.1007/978-3-319-75214-3_2
url http://hdl.handle.net/20.500.12272/8074
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2023-06-21T16:38:19Z
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 2023-06-21T16:38:19Z
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 Computer Science 790, 13-23. (2018)
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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score 13.24418

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