Optimization for an Uncertainty Reduction Method Applied to Forest Fires Spread Prediction

dc.creatorTardivo, María
dc.creatorCaymes Scutari, Paola
dc.creatorMéndez Garabetti, Miguel
dc.creatorBianchini, Germán
dc.date2023-06-21T16:38:19Z
dc.date2023-06-21T16:38:19Z
dc.date2018-01-01
dc.date.accessioned2023-08-31T14:40:22Z
dc.date.available2023-08-31T14:40:22Z
dc.identifierComputer Science
dc.identifierhttp://hdl.handle.net/20.500.12272/8074
dc.identifier10.1007/978-3-319-75214-3_2
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8548932
dc.descriptionForest fires prediction represents a great computational and mathematical challenge. The complexity lies both in the definition of mathematical models for describing the physical phenomenon and in the impossibility of measuring in real time all the parameters that determine the fire behaviour. ESSIM (Evolutionary Statistical System with Island Model) is an uncertainty reduction method that uses Statistic, High Performance Computing and Evolutionary Strategies in order to guide the search towards better solutions. ESSIM has been implemented with two different search strategies: the method ESSIM-EA uses Evolutionary Algorithms as optimization engine, whilst ESSIM-DE uses the Differential Evolution algorithm. ESSIM-EA has shown to obtain good quality of predictions, while ESSIM-DE obtains better response times. This article presents an alternative to improve the quality of solutions reached by ESSIM-DE, based on the analysis of the relationship between the evolutionary strategy convergence speed and the population distribution at the beginning of each prediction step.
dc.descriptionUniversidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina
dc.descriptionPeer Reviewed
dc.formatpdf
dc.languageeng
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.sourceComputer Science 790, 13-23. (2018)
dc.subjectForest fires ,Island model, Evolutionary Algorithms, Prediction, Differential Evolution, Parallelism
dc.titleOptimization for an Uncertainty Reduction Method Applied to Forest Fires Spread Prediction
dc.typeinfo:eu-repo/semantics/article
dc.typeacceptedVersion


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