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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)

García Nieto, P.J., García-Gonzalo, E., Bernardo Sánchez, A., Rodríguez Miranda, A.A.
Environmental modeling and assessment 2018 v.23 no.3 pp. 229-247
air quality, algorithms, carbon monoxide, data collection, dust, metropolitan areas, neural networks, nitrogen oxides, ozone, prediction, regression analysis, sulfur dioxide, Spain
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.