Increase in the quality of the prediction of a computational wildfire behavior methodthrough the improvement of the internal metaheuristic

dc.creatorMéndez Garabetti, Miguel
dc.creatorBianchini, Germán
dc.creatorCaymes Scutari, Paola
dc.creatorTardivo, María
dc.date2023-06-08T13:27:32Z
dc.date2023-06-08T13:27:32Z
dc.date2016-03-25
dc.date.accessioned2023-08-31T14:40:14Z
dc.date.available2023-08-31T14:40:14Z
dc.identifierFire Safety Journal (FSJ) (Vol 82)
dc.identifier0379-7112
dc.identifierhttp://hdl.handle.net/20.500.12272/8007
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8548908
dc.descriptionWildfires cause great losses and harms every year, some of which are often irreparable. Among the different strategies and technologies available to mitigate the effects of fire, wildfire behavior prediction may be a promising strategy. This approach allows for the identification of areas at greatest risk of being burned, thereby permitting to make decisions which in turn will help to reduce losses and damages. In this work we present an Evolutionary-Statistical System with Island Model, a new approach of the uncertainty reduction method Evolutionary-Statistical System. The operation of ESS is based on statistical analysis, parallel computing and Parallel Evolutionary Algorithms (PEA). ESS-IM empowers and broadens the search process and space by incorporating the Island Model in the metaheuristic stage (PEA), which increases the level of parallelism and, in fact, it permits to improve the quality of predictions.
dc.descriptionUniversidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina
dc.descriptionPeer Reviewed
dc.formatpdf
dc.languageeng
dc.relationPID 3939
dc.rightsopenAccess
dc.rightshttp://creativecommons.org/publicdomain/zero/1.0/
dc.rightsCC0 1.0 Universal
dc.rightsUniversidad Tecnológica Nacional. Facultad Regional Mendoza
dc.rightsAtribución
dc.sourceFire Safety Journal (FSJ) (82)49-62 (2016)
dc.subjectWildfire behavior prediction, Simulation, Uncertainty reduction, Parallel Evolutionary Algorithms, Statistical System
dc.titleIncrease in the quality of the prediction of a computational wildfire behavior methodthrough the improvement of the internal metaheuristic
dc.typeinfo:eu-repo/semantics/article
dc.typeacceptedVersion


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