Lopez Gonzales, Javier LinkolkGuerra Bendezu, Carlos AndresRomani Franco, Vivian Isabel2024-07-142024-07-142024-04-15http://repositorio.upeu.edu.pe/handle/20.500.12840/7716In this study, we propose a new hybrid method based on artificial neural networks to forecast daily extreme events of PM2.5 pollution concentration. The hybrid method combines self-organizing maps to identify temporal patterns of excessive daily pollution found at different monitoring stations, with a set of multilayer perceptron to forecast extreme values of PM2.5 for each cluster. The proposed model was applied to analyze five-year pollution data obtained from nine weather stations in the metropolitan area of Santiago, Chile. Simulation results show that the hybrid method improves the performance metrics when forecasting daily extreme values of PM2.5.application/pdfspainfo:eu-repo/semantics/embargoedAccessAir pollutionHybrid methodologyArtificial Neural NetworksTime series Forecastinginfo:eu-repo/semantics/bachelorThesishttp://purl.org/pe-repo/ocde/ford#1.01.03