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

Author

Listed:
  • 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
    Millennium Institute for Intelligent Healthcare Engineering (iHealth), Santiago 7820436, Chile)

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 .

Suggested Citation

  • Javier Linkolk López-Gonzales & Ana María Gómez Lamus & Romina Torres & Paulo Canas Rodrigues & Rodrigo Salas, 2023. "Self-Organizing Topological Multilayer Perceptron: A Hybrid Method to Improve the Forecasting of Extreme Pollution Values," Stats, MDPI, vol. 6(4), pages 1-19, November.
    • Handle: RePEc:gam:jstats:v:6:y:2023:i:4:p:77-1259:d:1278350
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    References listed on IDEAS

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    1. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
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