Semi-Supervised Air Quality Forecasting via Self-Supervised Hierarchical Graph Neural Network-Reference-Cited by-同舟云学术

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Semi-Supervised Air Quality Forecasting via Self-Supervised Hierarchical Graph Neural Network

Published:2023-05-01 Issue:5 Volume:35 Page:5230-5243
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Han Jindong¹^ORCID,Liu Hao²^ORCID,Xiong Haoyi³,Yang Jing⁴

Affiliation:

1. Artificial Intelligence Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong Province, China

2. Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong Province, China

3. Baidu Research, Beijing, China

4. Environmental Development Center, Ministry of Ecology and Environment, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/10091694/09709128.pdf?arnumber=9709128

Reference51 articles.

2. Inter-city air pollutant transport in The Beijing-Tianjin-Hebei urban agglomeration: Comparison between the winters of 2012 and 2016

3. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting;li,2017

4. Functional zoning for air quality

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