2023-06-122023-06-122016-10-14http://hdl.handle.net/20.500.12272/8036Forest fires are a critical natural hazard in many regions of the World. For this reason, the prediction of this kind of phenomenon is considered a very important task that involves a high degree of complexity and precision. The ability to predict the forest fire behaviour constitutes an important tool for managers, helping to improve the effectiveness of fire prevention, detection and firefighting resources allocation. For this reason, prediction methods should be configured to operate as efficiently as possible. In this paper, a calibration study of EvolutionaryStatistical System with Island Model’s evolutionary parameters is presented (ESS-IM). ESS-IM is a general-parallel uncertainty reduction method applied to the forest fires spread prediction. Index Terms—forest fire spread prediction, parallel evolutionary algorithms, parameters tuning, high performance computing.pdfspaopenAccesshttp://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 UniversalForest fire, Spread prediction, Parallel evolutionary algorithms, Parameters tuning, High performance, Computinginfo:eu-repo/semantics/articleUniversidad Tecnológica Nacional. Facultad Regional MendozaAtribución10.1109/SCCC.2016.7836007