Multi-scale spatiotemporal graph convolution network for air quality prediction

L Ge, K Wu, Y Zeng, F Chang, Y Wang, S Li - Applied Intelligence, 2021 - Springer
Air pollution is a serious environmental problem that has attracted much attention. Air quality
prediction can provide useful information for urban environmental governance decision …

Multi-view multi-task spatiotemporal graph convolutional network for air quality prediction

S Sui, Q Han - Science of The Total Environment, 2023 - Elsevier
Accurate air quality prediction is a crucial but arduous task for intelligent cities. Predictable
air quality can advise governments on environmental governance and residents on travel …

Self-adaptive spatial-temporal network based on heterogeneous data for air quality prediction

F Chang, L Ge, S Li, K Wu, Y Wang - Connection Science, 2021 - Taylor & Francis
With the development of society and the rise of people's environmental awareness, air
pollution is receiving increased public attention. Accurate air quality prediction can provide …

Spatial-temporal dynamic graph convolution neural network for air quality prediction

X Ouyang, Y Yang, Y Zhang… - 2021 International Joint …, 2021 - ieeexplore.ieee.org
Air quality prediction has received widespread attention from both the governments and
citizens due to its close relation to our lives. Analyzing the spatial relations and temporal …

Dynamic graph convolution neural network based on spatial-temporal correlation for air quality prediction

A Dun, Y Yang, F Lei - Ecological Informatics, 2022 - Elsevier
Air pollution is a serious threat to both the ecological environment and the physical health of
individuals. Therefore, accurate air quality prediction is urgent and necessary for pollution …

A novel spatiotemporal multigraph convolutional network for air pollution prediction

J Chen, C Yuan, S Dong, J Feng, H Wang - Applied Intelligence, 2023 - Springer
With the industrialization of society, air pollution has become a critical environmental issue,
leading to excessive morbidity and mortality from cardiovascular and respiratory diseases in …

Modeling inter-station relationships with attentive temporal graph convolutional network for air quality prediction

C Wang, Y Zhu, T Zang, H Liu, J Yu - … conference on web search and data …, 2021 - dl.acm.org
Air pollution is an important environmental issue of increasing concern, which impacts
human health. Accurate air quality prediction is crucial for avoiding people suffering from …

A dual-path dynamic directed graph convolutional network for air quality prediction

X Xiao, Z Jin, S Wang, J Xu, Z Peng, R Wang… - Science of The Total …, 2022 - Elsevier
Accurate air quality prediction can help cope with air pollution and improve the life quality.
With the development of the deployments of low-cost air quality sensors, increasing data …

[HTML][HTML] Deep spatio-temporal graph network with self-optimization for air quality prediction

XB Jin, ZY Wang, JL Kong, YT Bai, TL Su, HJ Ma… - Entropy, 2023 - mdpi.com
The environment and development are major issues of general concern. After much
suffering from the harm of environmental pollution, human beings began to pay attention to …

[HTML][HTML] Dynamic graph neural network with adaptive edge attributes for air quality prediction: A case study in China

J Xu, S Wang, N Ying, X Xiao, J Zhang, Z Jin, Y Cheng… - Heliyon, 2023 - cell.com
Air quality prediction is a typical Spatiotemporal modeling problem, which always uses
different components to handle spatial and temporal dependencies in complex systems …