AGConvLSTM: An Adaptive Graph Convolutional LSTM Network for Multi-Station Water Quality Classification

Water quality classification is essential for freshwater ecosystem protection but faces challenges posed by spatiotemporal dependencies and class imbalance. To address these issues, this paper proposes the Adaptive Graph Convolutional Long Short-Term Memory Network (AGConvLSTM), which integrates adaptive graph convolution into the LSTM gating mechanism to explicitly capture spatiotemporal dependencies. As complementary components, station-wise Principal Component Analysis (PCA) preserves spatial heterogeneity in feature structures, while DTW-SMOTE with adaptive sampling and dynamic denoising mitigates class imbalance. Evaluated on five-year water quality data from 13 stations in the Taihu Basin, China, AGConvLSTM achieves a test accuracy of 69.34% and an F1 score of 69.68%, outperforming baseline models. Station-wise accuracy ranges from 49.12% to 88.48%, reflecting spatial heterogeneity. These results suggest that spatiotemporal fusion within recurrent units provides an effective pathway for multi-station water quality classification and offers practical value for watershed early warning systems.