Temporal and spatial feature extraction using graph neural networks for multi-point water quality prediction in river network areas

Deep learning methods have demonstrated strong capabilities in capturing nonlinear relationships for water quality prediction, yet existing studies predominantly focus on individual monitoring sites while neglecting pollutant spatial dynamics. To address this limitation, a Spatio-Temporal Feature Graph Neural Network (STF-GNN) was proposed, which integrated graph convolutional networks (GCN), gated recurrent units (GRU), and self-attention mechanisms to explicitly model multi-scale spatiotemporal dependencies among distributed monitoring stations. By representing stations as graph nodes with adjacency relationships, STF-GNN could simultaneously extract spatial topological features and temporal evolution patterns from multivariate time series data. Experimental results demonstrated superior performance in dissolved oxygen (DO) and total nitrogen (TN) prediction, achieving RMSE values of 0.233 (DO) and 0.033 (TN), outperforming baseline models by 36.54–161.47 % in accuracy. Cross-basin validations revealed robust generalization capabilities of the established model, maintaining maximum relative errors below 0.639 (DO) and 0.606 (TN) without site-specific customization. Notably, the model achieved 88 % peak-valley synchronization at untrained station, demonstrating strong anti-interference ability against unseen environmental variations. Ablation studies confirmed the necessity of both spatial and temporal modules, with their omission causing significant accuracy declines (12.07–19.25 %). These findings highlighted the critical roles of both spatial and temporal feature extraction in improving predictive performance of the model. The work can provide a theoretically grounded framework for spatially-aware water quality prediction, supporting enhanced environmental monitoring strategies.