Enhanced water quality prediction by LSTM and graph attention network (L-GAT): An analytical study of the Pearl River Basin - Yueyi Liu; Hang Zheng; Jianshi Zhao (Water Research X, 2025)
Accurate water quality prediction plays a pivotal role in watershed management, yet it remains challenging due to the nonlinearity, non-stationarity, and multi-source variability of river systems. To address this, we propose L-GAT, a novel spatiotemporal forecasting approach that integrates Graph Attention Networks (GAT) with Long Short-Term Memory (LSTM) networks to jointly capture spatial heterogeneity and temporal dependencies. A directed graph containing 212 water quality stations and 58 meteorological stations in the Pearl River Basin was constructed, embedding upstream–downstream connections and meteorological influences. Experimental results demonstrate that the proposed L-GAT model significantly outperforms benchmark models, including GCN and GAT across four key water quality indicators: Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Ammonia Nitrogen (NH₃-N), and Total Phosphorus (TP). Specifically, L-GAT improves the NSE of DO prediction from 0.694 (GCN) to 0.783 and reduces the MSE by 28.8 %. For NH₃-N, L-GAT enhances the NSE by 4.4 % compared to GCN. Across all indicators, L-GAT achieves average NSE values above 0.77, with TP prediction reaching as high as 0.9289. Additionally, the model maintains robust performance under different sliding window sizes and forecast horizons, with a 3-day forecast achieving optimal stability. These findings highlight L-GAT’s superior ability to learn complex spatiotemporal patterns, making it a promising tool for large-scale water quality monitoring, early warning, and decision support in dynamic watershed environments.