Modeling Sulphur Dioxide (SO 2 ) Quality Levels of Jeddah City Using Machine Learning Approaches with Meteorological and Chemical Factors

Mohammed Alamoudi (), Osman Taylan, Behrooz Keshtegar, Mona Abusurrah and Mohammed Balubaid
Additional contact information
Mohammed Alamoudi: Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia
Osman Taylan: Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia
Behrooz Keshtegar: School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Mona Abusurrah: Department of Management Information Systems, College of Business Administration, Taibah University, P.O. Box 344, Al-Madinah 42353, Saudi Arabia
Mohammed Balubaid: Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia

Sustainability, 2022, vol. 14, issue 23, 1-21

Abstract: Modeling air quality in city centers is essential due to environmental and health-related issues. In this study, machine learning (ML) approaches were used to approximate the impact of air pollutants and metrological parameters on SO 2 quality levels. The parameters, NO, NO 2 , O 3 , PM10, RH, HyC, T, and P are significant factors affecting air pollution in Jeddah city. These factors were considered as the input parameters of the ANNs, MARS, SVR, and Hybrid model to determine the effect of those factors on the SO 2 quality level. Hence, ANN was employed to approximate the nonlinear relation between SO 2 and input parameters. The MARS approach has successful applications in air pollution predictions as an ML tool, employed in this study. The SVR approach was used as a nonlinear modeling tool to predict the SO 2 quality level. Furthermore, the MARS and SVR approaches were integrated to develop a novel hybrid modeling scheme for providing a nonlinear approximation of SO 2 concentration. The main innovation of this hybrid approach applied for predicting the SO 2 quality levels is to develop an efficient approach and reduce the time-consuming calibration processes. Four comparative statistical considerations, MAE, RMSE, NSE, and d, were applied to measure the accuracy and tendency. The hybrid SVR model outperforms the other models with the lowest RMSE and MAE, and the highest d and NSE in testing and training processes.

Keywords: air quality; sulphur dioxide pollution; machine learning; artificial neural networks (ANN); support vector regression (SVR); multivariate adaptive regression spline (MRS); environmental conditions (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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