Fine particulate matter concentration prediction based on hybrid convolutional network with aggregated local and global spatiotemporal information: A case study in …

Q Zeng, Y Cao, M Fan, L Chen, H Zhu, L Wang… - Atmospheric …, 2024 - Elsevier
Air pollution is a highly concerned environmental issue that have serious impacts on human
health and the ecological environment. Accurate air quality prediction can help people …

Fine-grained PM2. 5 prediction in Lanzhou based on the spatiotemporal graph convolutional network

Q Zhang, X Yu, R Guo, Y Qiao, Y Qi - Atmospheric Pollution Research, 2024 - Elsevier
Abstract Urban fine-scale PM 2. 5 concentrations can be predicted that considers both
spatial and temporal correlation, owing to the widespread installation of air quality micro …

Long-term Prediction Method for PM2. 5 Concentration Using Edge Channel Graph Attention Network and Gating Closed-form Continuous-time Neural Networks

C Zhang, X Li, H Sheng, Y Shen, W Xie… - Process Safety and …, 2024 - Elsevier
Fine particulate matter such as PM2. 5 threatens significantly to the environment and human
health, so it is essential to design a reliable long-term prediction method for PM2. 5 …

A hybrid model for spatial–temporal prediction of PM2.5 based on a time division method

B Liu, M Wang, HW Guesgen - International Journal of Environmental …, 2023 - Springer
In order to address the problem of air pollution along with the fast development of the
economy and an increase of urban population, this paper aims to capture the spatial …

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 …

[PDF][PDF] Air pollution prediction via graph attention network and gated recurrent unit

S Wang, L Qiao, W Fang, G Jing… - Computers, Materials …, 2022 - cdn.techscience.cn
PM2. 5 concentration prediction is of great significance to environmental protection and
human health. Achieving accurate prediction of PM2. 5 concentration has become an …

A Spatial–Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2.5 Concentration Prediction

S Lin, J Zhao, J Li, X Liu, Y Zhang, S Wang, Q Mei… - Entropy, 2022 - mdpi.com
Accurate and fine-grained prediction of PM2. 5 concentration is of great significance for air
quality control and human physical and mental health. Traditional approaches, such as time …

MGAtt-LSTM: A multi-scale spatial correlation prediction model of PM2. 5 concentration based on multi-graph attention

B Zhang, W Chen, MZ Li, X Guo, Z Zheng… - … Modelling & Software, 2024 - Elsevier
The increase in air pollution has posed numerous new challenges for human society,
making the exploration of an effective method for predicting air pollutant concentrations …

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 …

Forecasting PM2. 5 using hybrid graph convolution-based model considering dynamic wind-field to offer the benefit of spatial interpretability

H Zhou, F Zhang, Z Du, R Liu - Environmental Pollution, 2021 - Elsevier
Air pollution is a complex process and is affected by meteorological conditions and other
chemical components. Numerous studies have demonstrated that data-driven spatio …