A deep learning model with spatio-temporal graph convolutional networks for river water quality prediction

Abstract

High-precision water quality prediction plays a vital role in preventing and controlling river pollution. However, river water's highly nonlinear and complex spatio-temporal dependencies pose significant challenges to water quality prediction tasks. In order to capture the spatial and temporal characteristics of water quality data simultaneously, this paper combines deep learning algorithms for river water quality prediction in the river network area of Jiangnan Plain, China. A water quality prediction method based on graph convolutional network (GCN) and long short-term memory neural network (LSTM), namely spatio-temporal graph convolutional network model (ST-GCN), is proposed. Specifically, the spatio-temporal graph is constructed based on the spatio-temporal correlation between river stations, the spatial features in the river network are extracted using GCN, and the temporal correlation of water quality data is obtained by integrating LSTM. The model was evaluated using R2, MAE, and RMSE, and the experimental results were 0.977, 0.238, and 0.291, respectively. Compared with traditional water quality prediction models, the ST-GCN model has significantly improved prediction accuracy, better stability, and generalization ability. HIGHLIGHTS The spatio-temporal graph is used to characterize the spatio-temporal correlation of different monitoring stations.; Based on the GCN and the LSTM, the ST-GCN model is suitable for extracting spatial and temporal features in graph data.; In terms of river water quality prediction, the model has significantly improved performance compared with the five baseline models.;

Keywords

• changdang lake basin
• gcn
• high-precision prediction
• lstm
• spatio-temporal correlation