TY - GEN
T1 - Hybrid Water Quality Prediction with Graph Attention and Spatio-Temporal Fusion
AU - Lin, Yongze
AU - Qiao, Junfei
AU - Bi, Jing
AU - Yuan, Haitao
AU - Gao, Han
AU - Zhou, Meng Chu
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants 62073005 and 62173013, and the Fundamental Research Funds for the Central Universities under Grant YWF-22-L-1203.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Spatio-temporal prediction has a wide range of applications in many fields, e.g., air pollution, weather forecasting, and traffic forecasting. Water quality prediction is also one of spatio-temporal prediction tasks. However, it faces the following challenges: 1) Water quality in river networks has complex spatial dependencies; 2) There are complex nonlinear relations in water quality time series; and 3) It is difficult to realize long-term forecasting. To address these challenges, this work proposes a spatio-temporal prediction model called a Graph Attention-based Spatio-Temporal (GAST) neural network. GAST investigates spatial and temporal dependencies of water quality time series. First, we introduce a temporal attention mechanism to capture time series dependencies, which can effectively handle nonlinear relationships in time series. Second, we adopt a spatial attention mechanism to extract spatial dependencies of river networks and fuse temporal features of spatial nodes. Third, we adopt a temporal convolution residual mechanism based on the spatio-temporal fusion, which improves the accuracy of long-term series prediction. This work adopts two real-world datasets to evaluate the proposed GAST and experiments demonstrate that GAST outperforms several state-of-the-art methods in terms of prediction accuracy.
AB - Spatio-temporal prediction has a wide range of applications in many fields, e.g., air pollution, weather forecasting, and traffic forecasting. Water quality prediction is also one of spatio-temporal prediction tasks. However, it faces the following challenges: 1) Water quality in river networks has complex spatial dependencies; 2) There are complex nonlinear relations in water quality time series; and 3) It is difficult to realize long-term forecasting. To address these challenges, this work proposes a spatio-temporal prediction model called a Graph Attention-based Spatio-Temporal (GAST) neural network. GAST investigates spatial and temporal dependencies of water quality time series. First, we introduce a temporal attention mechanism to capture time series dependencies, which can effectively handle nonlinear relationships in time series. Second, we adopt a spatial attention mechanism to extract spatial dependencies of river networks and fuse temporal features of spatial nodes. Third, we adopt a temporal convolution residual mechanism based on the spatio-temporal fusion, which improves the accuracy of long-term series prediction. This work adopts two real-world datasets to evaluate the proposed GAST and experiments demonstrate that GAST outperforms several state-of-the-art methods in terms of prediction accuracy.
KW - graph attention
KW - river network
KW - spatio-temporal fusion
KW - temporal convolution residual
KW - Water quality prediction
UR - http://www.scopus.com/inward/record.url?scp=85142747015&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142747015&partnerID=8YFLogxK
U2 - 10.1109/SMC53654.2022.9945293
DO - 10.1109/SMC53654.2022.9945293
M3 - Conference contribution
AN - SCOPUS:85142747015
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 1419
EP - 1424
BT - 2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022
Y2 - 9 October 2022 through 12 October 2022
ER -