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Predicting Benzene Concentration Using Machine Learning and Time Series Algorithms

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  • Luis Alfonso Menéndez García

    (Department of Mining Technology, Topography and Structures. Higher and Technical School of Mining Engineering, University of León, Campus de Vegazana s/n, 24071 León, Spain)

  • Fernando Sánchez Lasheras

    (Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007 Oviedo, Spain)

  • Paulino José García Nieto

    (Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007 Oviedo, Spain)

  • Laura Álvarez de Prado

    (Department of Mining Technology, Topography and Structures. Higher and Technical School of Mining Engineering, University of León, Campus de Vegazana s/n, 24071 León, Spain)

  • Antonio Bernardo Sánchez

    (Department of Mining Technology, Topography and Structures. Higher and Technical School of Mining Engineering, University of León, Campus de Vegazana s/n, 24071 León, Spain)

Abstract

Benzene is a pollutant which is very harmful to our health, so models are necessary to predict its concentration and relationship with other air pollutants. The data collected by eight stations in Madrid (Spain) over nine years were analyzed using the following regression-based machine learning models: multivariate linear regression (MLR), multivariate adaptive regression splines (MARS), multilayer perceptron neural network (MLP), support vector machines (SVM), autoregressive integrated moving-average (ARIMA) and vector autoregressive moving-average (VARMA) models. Benzene concentration predictions were made from the concentration of four environmental pollutants: nitrogen dioxide (NO 2 ), nitrogen oxides (NO x ), particulate matter (PM 10 ) and toluene (C 7 H 8 ), and the performance measures of the model were studied from the proposed models. In general, regression-based machine learning models are more effective at predicting than time series models.

Suggested Citation

  • Luis Alfonso Menéndez García & Fernando Sánchez Lasheras & Paulino José García Nieto & Laura Álvarez de Prado & Antonio Bernardo Sánchez, 2020. "Predicting Benzene Concentration Using Machine Learning and Time Series Algorithms," Mathematics, MDPI, vol. 8(12), pages 1-22, December.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:12:p:2205-:d:460561
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    References listed on IDEAS

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    1. Mohammed Alamoudi & Osman Taylan & Behrooz Keshtegar & Mona Abusurrah & Mohammed Balubaid, 2022. "Modeling Sulphur Dioxide (SO 2 ) Quality Levels of Jeddah City Using Machine Learning Approaches with Meteorological and Chemical Factors," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    2. Chunli Huang & Xu Zhao & Weihu Cheng & Qingqing Ji & Qiao Duan & Yufei Han, 2022. "Statistical Inference of Dynamic Conditional Generalized Pareto Distribution with Weather and Air Quality Factors," Mathematics, MDPI, vol. 10(9), pages 1-25, April.

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