"Hybrid Water Quality Prediction with Graph Attention and Spatio-Tempor" by Yongze Lin, Junfei Qiao et al.
 

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

Document Type

Conference Proceeding

Publication Date

1-1-2022

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.

Identifier

85142747015 (Scopus)

ISBN

[9781665452588]

Publication Title

Conference Proceedings IEEE International Conference on Systems Man and Cybernetics

External Full Text Location

https://doi.org/10.1109/SMC53654.2022.9945293

ISSN

1062922X

First Page

1419

Last Page

1424

Volume

2022-October

Grant

62073005

Fund Ref

National Natural Science Foundation of China

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