Articles | Volume 15, issue 20
https://doi.org/10.5194/gmd-15-7791-2022
https://doi.org/10.5194/gmd-15-7791-2022
Development and technical paper
 | 
24 Oct 2022
Development and technical paper |  | 24 Oct 2022

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

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Short summary
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.