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Multi-Step Ahead Ex-Ante Forecasting of Air Pollutants Using Machine Learning

Author

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  • Snezhana Gocheva-Ilieva

    (Faculty of Mathematics and Informatics, Paisii Hilendarski University of Plovdiv, 24 Tzar Asen St, 4000 Plovdiv, Bulgaria)

  • Atanas Ivanov

    (Faculty of Mathematics and Informatics, Paisii Hilendarski University of Plovdiv, 24 Tzar Asen St, 4000 Plovdiv, Bulgaria)

  • Hristina Kulina

    (Faculty of Mathematics and Informatics, Paisii Hilendarski University of Plovdiv, 24 Tzar Asen St, 4000 Plovdiv, Bulgaria)

  • Maya Stoimenova-Minova

    (Faculty of Mathematics and Informatics, Paisii Hilendarski University of Plovdiv, 24 Tzar Asen St, 4000 Plovdiv, Bulgaria)

Abstract

In this study, a novel general multi-step ahead strategy is developed for forecasting time series of air pollutants. The values of the predictors at future moments are gathered from official weather forecast sites as independent ex-ante data. They are updated with new forecasted values every day. Each new sample is used to build- a separate single model that simultaneously predicts future pollution levels. The sought forecasts were estimated by averaging the actual predictions of the single models. The strategy was applied to three pollutants—PM 10 , SO 2 , and NO 2 —in the city of Pernik, Bulgaria. Random forest (RF) and arcing (Arc-x4) machine learning algorithms were applied to the modeling. Although there are many highly changing day-to-day predictors, the proposed averaging strategy shows a promising alternative to single models. In most cases, the root mean squared errors (RMSE) of the averaging models (aRF and aAR) for the last 10 horizons are lower than those of the single models. In particular, for PM 10 , the aRF’s RMSE is 13.1 vs. 13.8 micrograms per cubic meter for the single model; for the NO 2 model, the aRF exhibits 21.5 vs. 23.8; for SO 2, the aAR has 17.3 vs. 17.4; for NO 2 , the aAR’s RMSE is 22.7 vs. 27.5, respectively. Fractional bias is within the same limits of (−0.65, 0.7) for all constructed models.

Suggested Citation

  • Snezhana Gocheva-Ilieva & Atanas Ivanov & Hristina Kulina & Maya Stoimenova-Minova, 2023. "Multi-Step Ahead Ex-Ante Forecasting of Air Pollutants Using Machine Learning," Mathematics, MDPI, vol. 11(7), pages 1-26, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1566-:d:1105289
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    References listed on IDEAS

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    1. Daniel Vassallo & Raghavendra Krishnamurthy & Thomas Sherman & Harindra J. S. Fernando, 2020. "Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting," Energies, MDPI, vol. 13(20), pages 1-19, October.
    2. Kang, In-Bong, 2003. "Multi-period forecasting using different models for different horizons: an application to U.S. economic time series data," International Journal of Forecasting, Elsevier, vol. 19(3), pages 387-400.
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    1. Snezhana Gocheva-Ilieva & Atanas Ivanov & Hristina Kulina, 2023. "Special Issue “Statistical Data Modeling and Machine Learning with Applications II”," Mathematics, MDPI, vol. 11(12), pages 1-4, June.

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