Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Feng, Ruia; b; c; d; * | Huang, Cheng-Chene | Luo, Kuna | Zheng, Hui-Junf; *
Affiliations: [a] State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, P. R. China | [b] Hangzhou Engineering Consulting Center Co., Ltd, Hangzhou, P. R. China | [c] Zhejiang Academy of Ecological and Environmental Sciences, Hangzhou, P. R. China | [d] Hangzhou Knowledge Chain Technology Co., Ltd, Hangzhou, P. R. China | [e] Hangzhou Municipal Environmental Monitoring Central Station, Hangzhou, P. R. China | [f] Department of Critical Care Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, P. R. China
Correspondence: [*] Corresponding authors: Rui Feng. E-mail: [email protected], and Hui-jun Zheng. E-mail: [email protected].
Abstract: The West Lake of Hangzhou, a world famous landscape and cultural symbol of China, suffered from severe air quality degradation in January 2015. In this work, Random Forest (RF) and Recurrent Neural Networks (RNN) are used to analyze and predict air pollutants on the central island of the West Lake. We quantitatively demonstrate that the PM2.5 and PM10 were chiefly associated by the ups and downs of the gaseous air pollutants (SO2, NO2 and CO). Compared with the gaseous air pollutants, meteorological circumstances and regional transport played trivial roles in shaping PM. The predominant meteorological factor for SO2, NO2 and surface O3 was dew-point deficit. The proportion of sulfate in PM10 was higher than that in PM2.5. CO was strongly positively linked with PM. We discover that machine learning can accurately predict daily average wintertime SO2, NO2, PM2.5 and PM10, casting new light on the forecast and early warning of the high episodes of air pollutants in the future.
Keywords: Random forest, recurrent neural network, air pollutants prediction
DOI: 10.3233/JIFS-201964
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5215-5223, 2021
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]