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Predicting short-term PM 2.5 concentrations at fine temporal resolutions using a multi-branch temporal graph convolutional neural network

  • Published:2024-02-02 Issue: Volume: Page:1-24
  • ISSN:1365-8816
  • Container-title:International Journal of Geographical Information Science
  • language:en
  • Short-container-title:International Journal of Geographical Information Science

Author:

Guan Qingfeng12,Wang Jingyi3,Ren Shuliang4,Gao Huan12ORCID,Liang Zhewei12,Wang Junyi5,Yao Yao126

Affiliation:

1. School of Geography and Information Engineering, China University of Geoscience, Wuhan, PR China

2. National Engineering Research Center of GIS, China University of Geoscience, Wuhan, PR China

3. ChangJiang Waterway Bureau Survey Center, Wuhan, PR China

4. School of Earth and Space Sciences, Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing, PR China

5. Tencent Technology (Shenzhen) Co., Ltd., Shenzheng, PR China

6. Center for Spatial Information Science, The University of Tokyo, Chiba, Japan

Funder

National Key Research and Development Program of China

Publisher

Informa UK Limited

Subject

Library and Information Sciences,Geography, Planning and Development,Information Systems

Reference79 articles.

1. Reviewing the local and global implications of air pollution trends in Zaria, northern Nigeria;Aliyu Y.A.;Urban Climate,2018

2. DeepAirNet: applying recurrent networks for air quality prediction;Athira A.;Procedia Computer Science,2018

3. Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions;Bai Y.;Atmospheric Pollution Research,2016

4. Regression modeling for enterprise electricity consumption: A comparison of recurrent neural network and its variants;Bai Y.;International Journal of Electrical Power & Energy Systems,2021

5. BBQTS. 2011. The evaluation index system of urban road traffic operation. Beijing, China: BBQTS.
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