Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models

Autores
Denham, Mónica Malen; Laneri, Karina Fabiana
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Wildfires are a major concern in Argentinian northwestern Patagonia and in many ecosystems and human societies around the world. We developed an efficient cellular automata model in Graphic Processing Units (GPUs) to simulate fire propagation. The graphical advantages of GPUs were exploited by overlapping wind direction, as well as vegetation, slope, and aspect maps, taking into account relevant landscape characteristics for fire propagation. Stochastic propagation was performed with a probability model that depends on aspect, slope, wind direction and vegetation type. Implementing a Genetic Algorithm search strategy we show, using simulated fires, that we recover the five parameter values that characterize fire propagation. The efficiency of the fire simulation procedure allowed us to also estimate the fire ignition point when it is unknown as well as its associated uncertainty, making this approach suitable for the analysis of fire spread based on maps of burnt areas without knowing the point of origin of the fires or how they spread.
Fil: Denham, Mónica Malen. Universidad Nacional de Río Negro. Sede Andina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Laneri, Karina Fabiana. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
FOREST FIRE MODEL
FOREST FIRE SPREAD SIMULATIONS
GPU
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:11336/97288
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/97288
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation modelsDenham, Mónica MalenLaneri, Karina FabianaFOREST FIRE MODELFOREST FIRE SPREAD SIMULATIONSGPUhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Wildfires are a major concern in Argentinian northwestern Patagonia and in many ecosystems and human societies around the world. We developed an efficient cellular automata model in Graphic Processing Units (GPUs) to simulate fire propagation. The graphical advantages of GPUs were exploited by overlapping wind direction, as well as vegetation, slope, and aspect maps, taking into account relevant landscape characteristics for fire propagation. Stochastic propagation was performed with a probability model that depends on aspect, slope, wind direction and vegetation type. Implementing a Genetic Algorithm search strategy we show, using simulated fires, that we recover the five parameter values that characterize fire propagation. The efficiency of the fire simulation procedure allowed us to also estimate the fire ignition point when it is unknown as well as its associated uncertainty, making this approach suitable for the analysis of fire spread based on maps of burnt areas without knowing the point of origin of the fires or how they spread.Fil: Denham, Mónica Malen. Universidad Nacional de Río Negro. Sede Andina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Laneri, Karina Fabiana. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier2018-03info: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/11336/97288Denham, Mónica Malen; Laneri, Karina Fabiana; Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models; Elsevier; Journal of Computational Science; 25; 3-2018; 76-881877-7503CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jocs.2018.02.007info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1877750317308773info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:04:28Zoai:ri.conicet.gov.ar:11336/97288instacron: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:34982025-09-10 13:04:29.047CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models
title Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models
spellingShingle Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models
Denham, Mónica Malen
FOREST FIRE MODEL
FOREST FIRE SPREAD SIMULATIONS
GPU
title_short Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models
title_full Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models
title_fullStr Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models
title_full_unstemmed Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models
title_sort Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models
dc.creator.none.fl_str_mv Denham, Mónica Malen
Laneri, Karina Fabiana
author Denham, Mónica Malen
author_facet Denham, Mónica Malen
Laneri, Karina Fabiana
author_role author
author2 Laneri, Karina Fabiana
author2_role author
dc.subject.none.fl_str_mv FOREST FIRE MODEL
FOREST FIRE SPREAD SIMULATIONS
GPU
topic FOREST FIRE MODEL
FOREST FIRE SPREAD SIMULATIONS
GPU
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Wildfires are a major concern in Argentinian northwestern Patagonia and in many ecosystems and human societies around the world. We developed an efficient cellular automata model in Graphic Processing Units (GPUs) to simulate fire propagation. The graphical advantages of GPUs were exploited by overlapping wind direction, as well as vegetation, slope, and aspect maps, taking into account relevant landscape characteristics for fire propagation. Stochastic propagation was performed with a probability model that depends on aspect, slope, wind direction and vegetation type. Implementing a Genetic Algorithm search strategy we show, using simulated fires, that we recover the five parameter values that characterize fire propagation. The efficiency of the fire simulation procedure allowed us to also estimate the fire ignition point when it is unknown as well as its associated uncertainty, making this approach suitable for the analysis of fire spread based on maps of burnt areas without knowing the point of origin of the fires or how they spread.
Fil: Denham, Mónica Malen. Universidad Nacional de Río Negro. Sede Andina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Laneri, Karina Fabiana. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Wildfires are a major concern in Argentinian northwestern Patagonia and in many ecosystems and human societies around the world. We developed an efficient cellular automata model in Graphic Processing Units (GPUs) to simulate fire propagation. The graphical advantages of GPUs were exploited by overlapping wind direction, as well as vegetation, slope, and aspect maps, taking into account relevant landscape characteristics for fire propagation. Stochastic propagation was performed with a probability model that depends on aspect, slope, wind direction and vegetation type. Implementing a Genetic Algorithm search strategy we show, using simulated fires, that we recover the five parameter values that characterize fire propagation. The efficiency of the fire simulation procedure allowed us to also estimate the fire ignition point when it is unknown as well as its associated uncertainty, making this approach suitable for the analysis of fire spread based on maps of burnt areas without knowing the point of origin of the fires or how they spread.
publishDate 2018
dc.date.none.fl_str_mv 2018-03
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/11336/97288
Denham, Mónica Malen; Laneri, Karina Fabiana; Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models; Elsevier; Journal of Computational Science; 25; 3-2018; 76-88
1877-7503
CONICET Digital
CONICET
url http://hdl.handle.net/11336/97288
identifier_str_mv Denham, Mónica Malen; Laneri, Karina Fabiana; Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models; Elsevier; Journal of Computational Science; 25; 3-2018; 76-88
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.2018.02.007
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1877750317308773
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
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 13.004268

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