TY - JOUR
T1 - Air Pollution Prediction Via Graph Attention Network and Gated Recurrent Unit
AU - Wang, Shun
AU - Qiao, Lin
AU - Fang, Wei
AU - Jing, Guodong
AU - Sheng, Victor S.
AU - Zhang, Yong
N1 - Publisher Copyright:
© 2022 Tech Science Press. All rights reserved.
PY - 2022
Y1 - 2022
N2 - PM2.5 concentration prediction is of great significance to environmental protection and human health. Achieving accurate prediction of PM2.5 concentration has become an important research task. However, PM2.5 pollutants can spread in the earth’s atmosphere, causing mutual influence between different cities. To effectively capture the air pollution relationship between cities, this paper proposes a novel spatiotemporal model combining graph attention neural network (GAT) and gated recurrent unit (GRU), named GAT-GRU for PM2.5 concentration prediction. Specifically, GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities, and GRU is to extract the temporal dependence of the long-term data series. The proposed model integrates the learned spatio-temporal dependencies to capture long-term complex spatio-temporal features. Considering that air pollution is related to the meteorological conditions of the city, the knowledge acquired from meteorological data is used in the model to enhance PM2.5 prediction performance. The input of the GAT-GRU model consists of PM2.5 concentration data and meteorological data. In order to verify the effectiveness of the proposed GAT-GRU prediction model, this paper designs experiments on real-world datasets compared with other baselines. Experimental results prove that our model achieves excellent performance in PM2.5 concentration prediction.
AB - PM2.5 concentration prediction is of great significance to environmental protection and human health. Achieving accurate prediction of PM2.5 concentration has become an important research task. However, PM2.5 pollutants can spread in the earth’s atmosphere, causing mutual influence between different cities. To effectively capture the air pollution relationship between cities, this paper proposes a novel spatiotemporal model combining graph attention neural network (GAT) and gated recurrent unit (GRU), named GAT-GRU for PM2.5 concentration prediction. Specifically, GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities, and GRU is to extract the temporal dependence of the long-term data series. The proposed model integrates the learned spatio-temporal dependencies to capture long-term complex spatio-temporal features. Considering that air pollution is related to the meteorological conditions of the city, the knowledge acquired from meteorological data is used in the model to enhance PM2.5 prediction performance. The input of the GAT-GRU model consists of PM2.5 concentration data and meteorological data. In order to verify the effectiveness of the proposed GAT-GRU prediction model, this paper designs experiments on real-world datasets compared with other baselines. Experimental results prove that our model achieves excellent performance in PM2.5 concentration prediction.
KW - Air pollution prediction
KW - deep learning
KW - graph attention network
KW - spatiotemporal data modeling
UR - http://www.scopus.com/inward/record.url?scp=85130100918&partnerID=8YFLogxK
U2 - 10.32604/cmc.2022.028411
DO - 10.32604/cmc.2022.028411
M3 - Article
AN - SCOPUS:85130100918
SN - 1546-2218
VL - 73
SP - 673
EP - 687
JO - Computers, Materials and Continua
JF - Computers, Materials and Continua
IS - 1
ER -