A novel spatiotemporal multigraph convolutional network for air pollution prediction-Reference-Cited by-同舟云学术

A novel spatiotemporal multigraph convolutional network for air pollution prediction

Published:2023-01-27 Issue:15 Volume:53 Page:18319-18332
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Chen Jing,Yuan Changwei,Dong Shi^ORCID,Feng Jian,Wang Hujun

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-022-04418-y.pdf

Reference39 articles.

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2. Aditya C, Deshmukh CR, Nayana D et al (2018) Detection and prediction of air pollution using machine learning models. In: International journal of engineering trends and technology (IJETT), pp 204–207

3. Athira V, Geetha P, Vinayakumar R et al (2018) Deepairnet: applying recurrent networks for air quality prediction. Procedia Comput Sci 132:1394–1403

4. Chae S, Shin J, Kwon S et al (2021) PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network. Sci Reports 11(1):1–9

5. Chang FJ, Chang LC, Kang CC et al (2020) Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniques. Sci Total Environment 736:139,656

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