Kisi, Ozgur and Parmar, Kulwinder Singh and Soni, Kirti and Demir, Vahdettin (2017) Modeling of air pollutants using least square support vector regression, multivariate adaptive regression spline, and M5 model tree models. Air Quality, Atmosphere and Health, 10. pp. 873-883. ISSN 1873-9318

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This study investigates the applicability of three different soft computing methods, least square support vector regression (LSSVR), multivariate adaptive regression splines (MARS), and M5 Model Tree (M5-Tree), in forecasting SO2 concentration. These models were applied to monthly data obtained from Janakpuri, Nizamuddin, and Shahzadabad, located in Delhi, India. The models were compared with each other using the cross validation method with respect to root mean square error, mean absolute error, and correlation coefficient. According to the comparison, LSSVR provided better accuracy than the other models, while the MARS model was found to be the second best model in forecasting monthly SO2 concentration. Results indicated that the applied models gave better forecasting accuracy in Janakpuri station than the other stations. The results were also compared with previous studies and satisfactory results were obtained from three methods in modeling SO2 concentrations.

Item Type: Article
Additional Information: Copyright for this article belongs to M/s Springer Verlag.
Subjects: Earth Sciences
Depositing User: Users 27 not found.
Date Deposited: 12 Nov 2018 07:25
Last Modified: 12 Nov 2018 07:25

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