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Self-Organizing Topological Multilayer Perceptron: A Hybrid Method to Improve the Forecasting of Extreme Pollution Values
Author(s)
Gómez Lamus, Ana María
Escuela Colombiana de Ingenieria Julio Garavito
Torres, Romina
Adolfo Ibáñez University
Canas Rodrigues, Paulo
Universidade Federal da Bahia
Salas, Rodrigo
University of Valparaíso
Date Issued
11 de noviembre de 2023
Type
Article
Volume
6
Issue
4
Series/Report No.
Stats
Start Page
1241
End Page
1259
ISSN
2571-905X
Abstract
Forecasting air pollutant levels is essential in regulatory plans focused on controlling and mitigating air pollutants, such as particulate matter. Focusing the forecast on air pollution peaks is challenging and complex since the pollutant time series behavior is not regular and is affected by several environmental and urban factors. In 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 performance metrics when forecasting daily extreme values of PM2.5.