The Role of GARCH Effect on the Prediction of Air Pollution

Kai-Chao Yao, Hsiu-Wen Hsueh, Ming-Hsiang Huang and Tsung-Che Wu
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Kai-Chao Yao: Department of Industrial Education and Technology, National Changhua University of Education, Changhua City 50007, Taiwan
Hsiu-Wen Hsueh: Department of Industrial Education and Technology, National Changhua University of Education, Changhua City 50007, Taiwan
Ming-Hsiang Huang: Department of Business Administration, National Changhua University of Education, Changhua City 50007, Taiwan
Tsung-Che Wu: Department of Banking and Finance, National Chiayi University, Chiayi City 60054, Taiwan

Sustainability, 2022, vol. 14, issue 8, 1-20

Abstract: Air pollution prediction is an important issue for regulators and practitioners in a sustainable era. Air pollution, especially PM 2.5 resulting from industrialization, has fostered a wave of global weather migration and jeopardized human health in the past three decades. Taiwan has evolved as a highly developed economy and has a severe PM 2.5 pollution problem. Thus, the control of PM 2.5 is a critical issue for regulators, practitioners and academics. More recently, GA-SVM, an artificial-intelligence-based approach, has become a preferred prediction model, attributed to the advances in computer technology. However, hourly observation of PM 2.5 concentration tends to present the GARCH effect. The objective of this study is to explore whether the integration of GA-SVM with the GARCH model can build a more accurate air pollution prediction model. The study adopts central Taiwan, the region with the worst level of PM 2.5 , as the source of observations. The empirical implementation of this study took a two-step approach; first, we examined the potential existence of the GARCH effect on the observed PM 2.5 data. Second, we built a GA-SVM model integrated with the GARCH framework to predict the 8 h PM 2.5 concentration of the sample region. The empirical results indicate that the prediction performance of our proposed alternative model outperformed the traditional SVM and GA-SVM models in terms of both MAPE and RMSE. The findings in this study provide evidence to support our expectation that adopting the SVM-based approach model for PM 2.5 prediction is appropriate, and that prediction performance can be improved by integrating the GARCH model. Moreover, consistent with our prior expectation, the evidence further supports that taking the GARCH effect into account in the GA-SVM model significantly improves the accuracy of prediction. To the knowledge of the authors, this study is the first to attempt to integrate the GARCH effect into the GA-SVM model in the prediction of PM 2.5 . In summary, with regard to the development of sustainability for both regulators and practitioners, our results strongly encourage them to take the GARCH effect into consideration in air pollution prediction if a regression-based model is to be adopted. Furthermore, this study may shed light on the application of the GARCH model and SVM models in the air pollution prediction literature.

Keywords: GA-SVM; GARCH; PM 2.5 prediction; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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