2023-06-062023-06-062017-06-12International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerlandhttp://hdl.handle.net/20.500.12272/7952Predicting the propagation of forest fires is a crucial point to mitigate their effects. Therefore, several computational tools or simulators have been developed to predict the fire ropagation. Such tools consider the scenario (topography, vegetation types, fire front situation), and the particular conditions where the fire is evolving (vegetation conditions, meteorological conditions) estimate precisely, and there is a high degree of uncertainty in many of them. This uncer-tainty provokes a certain lack of accuracy in the predictions with the consequent risks. So, it to predict the fire propagation. However, these parameters are usually difficult to measure or is necessary to apply methods to reduce the uncertainty in the input parameters. This work presents a comparison of ESSIM-EA and ESSIM-DE: two methods to reduce the uncertainty in the input parameters. These methods combine Evolutionary Algorithms, Parallelism and Statistical Analysis to improve the propagation prediction.pdfengopenAccesshttp://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 UniversalForest Fire Prediction, Statistical analysis, Evolutionary Algorithms, Islands model, High Performance Computinginfo:eu-repo/semantics/articleUniversidad Tecnológica Nacional. Facultad Regional MendozaAtribución10.1016/j.procs.2017.05.252.