Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China
Li Fang,Jianbing Jin,Arjo Segers,Hai Xiang Lin,Mijie Pang,Cong Xiao,Tuo Deng,and Hong Liao
Li Fang
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
Cong Xiao
Key Laboratory of Petroleum Engineering, Ministry of Education, China University of Petroleum, Beijing, China
Tuo Deng
Delft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
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Total article views: 3,658 (including HTML, PDF, and XML)
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Total article views: 2,851 (including HTML, PDF, and XML)
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Total article views: 807 (including HTML, PDF, and XML)
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This study proposes a regional feature selection-based machine learning system to predict short-term air quality in China. The system has a tool that can figure out the importance of input data for better prediction. It provides large-scale air quality prediction that exhibits improved interpretability, fewer training costs, and higher accuracy compared with a standard machine learning system. It can act as an early warning for citizens and reduce exposure to PM2.5 and other air pollutants.
This study proposes a regional feature selection-based machine learning system to predict...
