A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction

Autores
Tardivo, María; Caymes Scutari, Paola; Bianchini, Germán; Méndez Garabetti, Miguel; Cencerrado, Andrés; Cortés, Ana
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
Predicting the propagation of forest fires is a crucial point to mitigate their effects. Therefore, several computational tools or simulators have been developed to predict the fire ropagation. Such tools consider the scenario (topography, vegetation types, fire front situation), and the particular conditions where the fire is evolving (vegetation conditions, meteorological conditions) estimate precisely, and there is a high degree of uncertainty in many of them. This uncer-tainty provokes a certain lack of accuracy in the predictions with the consequent risks. So, it to predict the fire propagation. However, these parameters are usually difficult to measure or is necessary to apply methods to reduce the uncertainty in the input parameters. This work presents a comparison of ESSIM-EA and ESSIM-DE: two methods to reduce the uncertainty in the input parameters. These methods combine Evolutionary Algorithms, Parallelism and Statistical Analysis to improve the propagation prediction.
Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina
Fuente
Procedia Computer Science (nª 108): 2018-2027 (2017)
Materia
Forest Fire Prediction, Statistical analysis, Evolutionary Algorithms, Islands model, High Performance Computing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2023-06-06T14:33:08Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/7952
Acceder
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network_acronym_str RIAUTN
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network_name_str Repositorio Institucional Abierto (UTN)
spelling A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation predictionTardivo, MaríaCaymes Scutari, PaolaBianchini, GermánMéndez Garabetti, MiguelCencerrado, AndrésCortés, AnaForest Fire Prediction, Statistical analysis, Evolutionary Algorithms, Islands model, High Performance ComputingPredicting the propagation of forest fires is a crucial point to mitigate their effects. Therefore, several computational tools or simulators have been developed to predict the fire ropagation. Such tools consider the scenario (topography, vegetation types, fire front situation), and the particular conditions where the fire is evolving (vegetation conditions, meteorological conditions) estimate precisely, and there is a high degree of uncertainty in many of them. This uncer-tainty provokes a certain lack of accuracy in the predictions with the consequent risks. So, it to predict the fire propagation. However, these parameters are usually difficult to measure or is necessary to apply methods to reduce the uncertainty in the input parameters. This work presents a comparison of ESSIM-EA and ESSIM-DE: two methods to reduce the uncertainty in the input parameters. These methods combine Evolutionary Algorithms, Parallelism and Statistical Analysis to improve the propagation prediction.Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina2023-06-06T14:33:08Z2023-06-06T14:33:08Z2017-06-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfInternational Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerlandhttp://hdl.handle.net/20.500.12272/795210.1016/j.procs.2017.05.252.Procedia Computer Science (nª 108): 2018-2027 (2017)reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica NacionalengPID 3939info:eu-repo/semantics/openAccess2023-06-06T14:33:08Zhttp://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 UniversalUniversidad Tecnológica Nacional. Facultad Regional MendozaAtribución2026-09-24T12:48:03Zoai:ria.utn.edu.ar:20.500.12272/7952instacron: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 12:48:04.364Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction
title A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction
spellingShingle A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction
Tardivo, María
Forest Fire Prediction, Statistical analysis, Evolutionary Algorithms, Islands model, High Performance Computing
title_short A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction
title_full A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction
title_fullStr A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction
title_full_unstemmed A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction
title_sort A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction
dc.creator.none.fl_str_mv Tardivo, María
Caymes Scutari, Paola
Bianchini, Germán
Méndez Garabetti, Miguel
Cencerrado, Andrés
Cortés, Ana
author Tardivo, María
author_facet Tardivo, María
Caymes Scutari, Paola
Bianchini, Germán
Méndez Garabetti, Miguel
Cencerrado, Andrés
Cortés, Ana
author_role author
author2 Caymes Scutari, Paola
Bianchini, Germán
Méndez Garabetti, Miguel
Cencerrado, Andrés
Cortés, Ana
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Forest Fire Prediction, Statistical analysis, Evolutionary Algorithms, Islands model, High Performance Computing
topic Forest Fire Prediction, Statistical analysis, Evolutionary Algorithms, Islands model, High Performance Computing
dc.description.none.fl_txt_mv Predicting the propagation of forest fires is a crucial point to mitigate their effects. Therefore, several computational tools or simulators have been developed to predict the fire ropagation. Such tools consider the scenario (topography, vegetation types, fire front situation), and the particular conditions where the fire is evolving (vegetation conditions, meteorological conditions) estimate precisely, and there is a high degree of uncertainty in many of them. This uncer-tainty provokes a certain lack of accuracy in the predictions with the consequent risks. So, it to predict the fire propagation. However, these parameters are usually difficult to measure or is necessary to apply methods to reduce the uncertainty in the input parameters. This work presents a comparison of ESSIM-EA and ESSIM-DE: two methods to reduce the uncertainty in the input parameters. These methods combine Evolutionary Algorithms, Parallelism and Statistical Analysis to improve the propagation prediction.
Fil: Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina
description Predicting the propagation of forest fires is a crucial point to mitigate their effects. Therefore, several computational tools or simulators have been developed to predict the fire ropagation. Such tools consider the scenario (topography, vegetation types, fire front situation), and the particular conditions where the fire is evolving (vegetation conditions, meteorological conditions) estimate precisely, and there is a high degree of uncertainty in many of them. This uncer-tainty provokes a certain lack of accuracy in the predictions with the consequent risks. So, it to predict the fire propagation. However, these parameters are usually difficult to measure or is necessary to apply methods to reduce the uncertainty in the input parameters. This work presents a comparison of ESSIM-EA and ESSIM-DE: two methods to reduce the uncertainty in the input parameters. These methods combine Evolutionary Algorithms, Parallelism and Statistical Analysis to improve the propagation prediction.
publishDate 2017
dc.date.none.fl_str_mv 2017-06-12
2023-06-06T14:33:08Z
2023-06-06T14:33:08Z
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 International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland
http://hdl.handle.net/20.500.12272/7952
10.1016/j.procs.2017.05.252.
identifier_str_mv International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland
10.1016/j.procs.2017.05.252.
url http://hdl.handle.net/20.500.12272/7952
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PID 3939
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2023-06-06T14:33:08Z
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 2023-06-06T14:33:08Z
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 Procedia Computer Science (nª 108): 2018-2027 (2017)
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
_version_ 1877230952298577920
score 13.24418

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