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dc.contributor.author
Denham, Mónica Malen
dc.contributor.author
Laneri, Karina Fabiana
dc.date.available
2020-02-12T17:56:07Z
dc.date.issued
2018-03
dc.identifier.citation
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
dc.identifier.issn
1877-7503
dc.identifier.uri
http://hdl.handle.net/11336/97288
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
FOREST FIRE MODEL
dc.subject
FOREST FIRE SPREAD SIMULATIONS
dc.subject
GPU
dc.subject.classification
Ingeniería de Sistemas y Comunicaciones
dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-10-15T17:29:45Z
dc.journal.volume
25
dc.journal.pagination
76-88
dc.journal.pais
Países Bajos
dc.description.fil
Fil: Denham, Mónica Malen. Universidad Nacional de Río Negro. Sede Andina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Laneri, Karina Fabiana. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Journal of Computational Science
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.jocs.2018.02.007
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1877750317308773
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