Hybrid Water Quality Prediction with Graph Attention and Spatio-Temporal Fusion

Yongze Lin, Junfei Qiao, Jing Bi, Haitao Yuan, Han Gao, Meng Chu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1419-1424
Number of pages6
ISBN (Electronic)9781665452588
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 - Prague, Czech Republic
Duration: Oct 9 2022Oct 12 2022

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2022-October
ISSN (Print)1062-922X

Conference

Conference2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022
Country/TerritoryCzech Republic
CityPrague
Period10/9/2210/12/22

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

Keywords

  • Water quality prediction
  • graph attention
  • river network
  • spatio-temporal fusion
  • temporal convolution residual

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