Hybrid interpretable predictive machine learning model for air pollution prediction

Author:

Gu Yuanlin,Li Baihua,Meng Qinggang

Funder

Newton Fund

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference49 articles.

1. World Health Organization, (n.d.). http://www9.who.int/airpollution/en/.

2. OECD, (n.d.). https://www.oecd.org/environment/indicators-modelling-outlooks/.

3. Beijing’s Air Quality Continues to Show Significant Improvement;Davidson;Eco Watch.,2020

4. The characteristics of PM2.5 in Beijing, China;He;Atmos. Environ.,2001

5. P.-H. Yang, M.-T. Hsieh, G.-M. Lin, M.-J. Chen, C.-H. Yeh, Z.-X. Huang, C.-M. Yang, Prediction of Outpatient Visits for Upper Respiratory Tract Infections by Machine Learning of PM2.5 and PM10 Levels in Taiwan, in: 2018 IEEE Int. Conf. Consum. Electron., 2018.

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