Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China

Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China

Fang, Li (Nanjing University of Information Sciences and Technology)
Jin, Jianbing (Nanjing University of Information Sciences and Technology)
Segers, Arjo (TNO)
Lin, H.X. (TU Delft Mathematical Physics; Universiteit Leiden)
Pang, Mijie (Nanjing University of Information Sciences and Technology)
Xiao, Cong (China University of Petroleum - Beijing)
Deng, T. (TU Delft Mathematical Physics)
Liao, Hong (Nanjing University of Information Sciences and Technology)

2022

With the explosive growth of atmospheric data, machine learning models have achieved great success in air pollution forecasting because of their higher computational efficiency than the traditional chemical transport models. However, in previous studies, new prediction algorithms have only been tested at stations or in a small region; a large-scale air quality forecasting model remains lacking to date. Huge dimensionality also means that redundant input data may lead to increased complexity and therefore the over-fitting of machine learning models. Feature selection is a key topic in machine learning development, but it has not yet been explored in atmosphere-related applications. In this work, a regional feature selection-based machine learning (RFSML) system was developed, which is capable of predicting air quality in the short term with high accuracy at the national scale. Ensemble-Shapley additive global importance analysis is combined with the RFSML system to extract significant regional features and eliminate redundant variables at an affordable computational expense. The significance of the regional features is also explained physically. Compared with a standard machine learning system fed with relative features, the RFSML system driven by the selected key features results in superior interpretability, less training time, and more accurate predictions. This study also provides insights into the difference in interpretability among machine learning models (i.e., random forest, gradient boosting, and multi-layer perceptron models).

http://resolver.tudelft.nl/uuid:e603db54-d4b4-480c-9445-1d51cbe3ab23

https://doi.org/10.5194/gmd-15-7791-2022

1991-959X

Geoscientific Model Development, 15 (20), 7791-7807

Institutional Repository

journal article

© 2022 Li Fang, Jianbing Jin, Arjo Segers, H.X. Lin, Mijie Pang, Cong Xiao, T. Deng, Hong Liao