Self-Organizing Topological Multilayer Perceptron: A Hybrid Method to Improve the Forecasting of Extreme Pollution Values

Javier Linkolk López-Gonzales, Ana María Gómez Lamus, Romina Torres, Paulo Canas Rodrigues () and Rodrigo Salas
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Javier Linkolk López-Gonzales: Escuela de Posgrado, Universidad Peruana Unión, Lima 15468, Peru
Ana María Gómez Lamus: Statistical Engineering, Escuela Colombiana de Ingeniería Julio Garavito, Bogotá 111166, Colombia
Romina Torres: Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Viña del Mar 2562340, Chile
Paulo Canas Rodrigues: Department of Statistics, Federal University of Bahia, Salvador 40170-110, Brazil
Rodrigo Salas: Escuela de Ingeniería C. Biomédica, Universidad de Valparaíso, Valparaíso 2362905, Chile

Stats, 2023, vol. 6, issue 4, 1-19

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 PM 2.5 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 PM 2.5 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 PM 2.5 .

Keywords: air pollution; hybrid methodology; artificial neural networks; time series forecasting (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2023
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