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Air Quality Modeling Using the PSO-SVM-Based Approach, MLP Neural Network, and M5 Model Tree in the Metropolitan Area of Oviedo (Northern Spain)

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Abstract

The main aim of this study was to construct several regression models of air quality using techniques based on the statistical learning, in the metropolitan area of Oviedo, in northern Spain. In this research, a hybrid particle swarm optimization-based evolutionary support vector regression is implemented to predict the air quality from the experimental dataset (specifically, nitrogen oxides, carbon monoxide, sulfur dioxide, ozone, and dust) collected from 2013 to 2015 in the metropolitan area of Oviedo. Furthermore, a multilayer perceptron network (MLP) and the M5 model tree were also fitted to the experimental dataset for comparison purposes. Finally, the predicted results show that the hybrid proposed model is more robust than the MLP and M5 model tree prediction methods in terms of statistical estimators and testing performances.

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Acknowledgements

The authors wish to acknowledge the computational support provided by the Department of Mathematics at University of Oviedo. Additionally, we would like to thank Anthony Ashworth for his revision of the English grammar and spelling of the manuscript.

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Authors and Affiliations

  1. Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007, Oviedo, Spain

    P. J. García Nieto & E. García-Gonzalo

  2. Department of Mining Technology, Topography and Structures, University of León, 24071, León, Spain

    A. Bernardo Sánchez & A. A. Rodríguez Miranda

Authors
  1. P. J. García Nieto
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  2. E. García-Gonzalo
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  3. A. Bernardo Sánchez
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  4. A. A. Rodríguez Miranda
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Correspondence to P. J. García Nieto.

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García Nieto, P.J., García-Gonzalo, E., Bernardo Sánchez, A. et al. Air Quality Modeling Using the PSO-SVM-Based Approach, MLP Neural Network, and M5 Model Tree in the Metropolitan Area of Oviedo (Northern Spain). Environ Model Assess 23, 229–247 (2018). https://doi.org/10.1007/s10666-017-9578-y

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