Dynamic Tuning of a Forest Fire Prediction Parallel Method

Autores
Caymes Scutari, Paola; Tardivo, María; Bianchini, Germán; Méndez Garabetti, Miguel
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
Different parameters feed mathematical and/or empirical models. However, the uncertainty (or lack of precision) present in such parameters usually impacts in the quality of the output/recommendation of prediction models. Fortunately, there exist uncertainty reduction methods which enable the obtention of more accurate solutions. One of such methods is ESSIM-DE (Evolutionary Statistical System with Island Model and Differential Evolution), a general purpose method for prediction and uncertainty reduction. ESSIM-DE has been used for the forest fireline prediction, and it is based on statistical analysis, parallel computing, and differential evolution. In this work, we enrich ESSIM-DE with an automatic and dynamic tuning strategy, to adapt the generational parameter of the evolutionary process in order to avoid premature convergence and/or stagnation, and to improve the general performance of the predictive tool. We describe the metrics, the tuning points and actions, and we show the results for different controlled fires.
Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina
Peer Reviewed
Fuente
Computer Science 1184, 19-34. (2020)
Materia
Dynamic tuning, Fire prediction, Differential Evolution, Parallel computing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2023-06-21T16:11:11Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/8073
Acceder
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network_acronym_str RIAUTN
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network_name_str Repositorio Institucional Abierto (UTN)
spelling Dynamic Tuning of a Forest Fire Prediction Parallel MethodCaymes Scutari, PaolaTardivo, MaríaBianchini, GermánMéndez Garabetti, MiguelDynamic tuning, Fire prediction, Differential Evolution, Parallel computingDifferent parameters feed mathematical and/or empirical models. However, the uncertainty (or lack of precision) present in such parameters usually impacts in the quality of the output/recommendation of prediction models. Fortunately, there exist uncertainty reduction methods which enable the obtention of more accurate solutions. One of such methods is ESSIM-DE (Evolutionary Statistical System with Island Model and Differential Evolution), a general purpose method for prediction and uncertainty reduction. ESSIM-DE has been used for the forest fireline prediction, and it is based on statistical analysis, parallel computing, and differential evolution. In this work, we enrich ESSIM-DE with an automatic and dynamic tuning strategy, to adapt the generational parameter of the evolutionary process in order to avoid premature convergence and/or stagnation, and to improve the general performance of the predictive tool. We describe the metrics, the tuning points and actions, and we show the results for different controlled fires.Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; ArgentinaPeer Reviewed2023-06-21T16:11:11Z2023-06-21T16:11:11Z2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfSpringer Nature Switzerland AG 2020http://hdl.handle.net/20.500.12272/807310.1007/978-3-030-48325-8_2Computer Science 1184, 19-34. (2020)reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacionalenginfo:eu-repo/semantics/openAccess2023-06-21T16:11:11Zhttp://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 UniversalUniversidad Tecnológica Nacional. Facultad Regional MendozaAtribución2026-10-01T11:59:19Zoai:ria.utn.edu.ar:20.500.12272/8073instacron: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:a2026-10-01 11:59:20.186Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Dynamic Tuning of a Forest Fire Prediction Parallel Method
title Dynamic Tuning of a Forest Fire Prediction Parallel Method
spellingShingle Dynamic Tuning of a Forest Fire Prediction Parallel Method
Caymes Scutari, Paola
Dynamic tuning, Fire prediction, Differential Evolution, Parallel computing
title_short Dynamic Tuning of a Forest Fire Prediction Parallel Method
title_full Dynamic Tuning of a Forest Fire Prediction Parallel Method
title_fullStr Dynamic Tuning of a Forest Fire Prediction Parallel Method
title_full_unstemmed Dynamic Tuning of a Forest Fire Prediction Parallel Method
title_sort Dynamic Tuning of a Forest Fire Prediction Parallel Method
dc.creator.none.fl_str_mv Caymes Scutari, Paola
Tardivo, María
Bianchini, Germán
Méndez Garabetti, Miguel
author Caymes Scutari, Paola
author_facet Caymes Scutari, Paola
Tardivo, María
Bianchini, Germán
Méndez Garabetti, Miguel
author_role author
author2 Tardivo, María
Bianchini, Germán
Méndez Garabetti, Miguel
author2_role author
author
author
dc.subject.none.fl_str_mv Dynamic tuning, Fire prediction, Differential Evolution, Parallel computing
topic Dynamic tuning, Fire prediction, Differential Evolution, Parallel computing
dc.description.none.fl_txt_mv Different parameters feed mathematical and/or empirical models. However, the uncertainty (or lack of precision) present in such parameters usually impacts in the quality of the output/recommendation of prediction models. Fortunately, there exist uncertainty reduction methods which enable the obtention of more accurate solutions. One of such methods is ESSIM-DE (Evolutionary Statistical System with Island Model and Differential Evolution), a general purpose method for prediction and uncertainty reduction. ESSIM-DE has been used for the forest fireline prediction, and it is based on statistical analysis, parallel computing, and differential evolution. In this work, we enrich ESSIM-DE with an automatic and dynamic tuning strategy, to adapt the generational parameter of the evolutionary process in order to avoid premature convergence and/or stagnation, and to improve the general performance of the predictive tool. We describe the metrics, the tuning points and actions, and we show the results for different controlled fires.
Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina
Peer Reviewed
description Different parameters feed mathematical and/or empirical models. However, the uncertainty (or lack of precision) present in such parameters usually impacts in the quality of the output/recommendation of prediction models. Fortunately, there exist uncertainty reduction methods which enable the obtention of more accurate solutions. One of such methods is ESSIM-DE (Evolutionary Statistical System with Island Model and Differential Evolution), a general purpose method for prediction and uncertainty reduction. ESSIM-DE has been used for the forest fireline prediction, and it is based on statistical analysis, parallel computing, and differential evolution. In this work, we enrich ESSIM-DE with an automatic and dynamic tuning strategy, to adapt the generational parameter of the evolutionary process in order to avoid premature convergence and/or stagnation, and to improve the general performance of the predictive tool. We describe the metrics, the tuning points and actions, and we show the results for different controlled fires.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01
2023-06-21T16:11:11Z
2023-06-21T16:11:11Z
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 Springer Nature Switzerland AG 2020
http://hdl.handle.net/20.500.12272/8073
10.1007/978-3-030-48325-8_2
identifier_str_mv Springer Nature Switzerland AG 2020
10.1007/978-3-030-48325-8_2
url http://hdl.handle.net/20.500.12272/8073
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2023-06-21T16:11:11Z
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 2023-06-21T16:11:11Z
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 1184, 19-34. (2020)
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 13.364332

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