Enhanced Spatiotemporal Relationship-Guided Deep Learning for Water Quality Prediction

Abstract

Water quality prediction serves as a crucial basis for water environment supervision and is of great significance for water resource protection. This study utilized meteorological and water quality data from 40 monitoring stations in the Tuojiang River Basin, Sichuan Province, China. A Gated Recurrent Unit (GRU) model and a Graph Attention Network–Gated Recurrent Unit (GAT-GRU) model were constructed. Furthermore, based on the GAT-GRU framework, an Enhanced Spatio-Temporal Relation-Guided Gated Recurrent Unit (ESRG-GRU) model was developed by incorporating an explicit river network topology and a loss function that is sensitive to extreme values to strengthen spatio-temporal relationships. Water quality predictions were made for all 40 stations, and the performance of the three models was compared. The results show that, during the 7-day forecasting period, the training time of both the ESRG-GRU and the GAT-GRU models was only about 1/40 of that required for the GRU model. In terms of prediction accuracy, the average Nash–Sutcliffe efficiency (NSE) values over the 7-day forecast period were ESRG-GRU (0.7904) > GAT-GRU (0.7557) > GRU (0.6870), while the average root mean square error (RMSE) values were ESRG-GRU (0.0156) < GAT-GRU (0.0168) < GRU (0.0185). Regarding accuracy across different regions and seasons within the river basin, the ESRG-GRU model, guided by enhanced spatio-temporal deep learning, consistently outperformed both the GRU and the GAT-GRU models. This method can effectively enhance both the efficiency and accuracy of water quality prediction, thereby providing support for water environment supervision and regional water quality improvement.