Water Quality Anomaly Detection Method Based on Attention-Gated Liquid Neural Network

With the rapid expansion of water monitoring networks, time-series anomaly detection has become increasingly crucial for safeguarding aquatic environments and ensuring their sustainable management. However, conventional models often struggle with irregular sampling intervals, multivariate correlations, and interpretability in practical applications. To address these challenges, this study proposes an Attention Gated-Liquid Neural Network (AG-LNN) that integrates the dynamic modeling capability of the Liquid Neural Network (LNN) with attention-based gating mechanisms. The model introduces an input-attention gate to emphasize anomaly-relevant variables such as Dissolved Oxygen (DO) and the Permanganate Index (CODMn), and a time-constant gate that adaptively adjusts the model's temporal memory. Using data from the China National Environmental Monitoring Center (CNEMC) collected between 2019 and 2024 across 13 provinces, AG-LNN demonstrated superior performance over baseline models, including Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), Transformer, and Graph Neural Network (GNN) architectures. It achieves a Precision-Recall Area Under Curve (PR-AUC) of 0.95 and an F1-score of 0.90, while maintaining stability under cross-region and temporal evaluations. A compact version, AG-LNN-light, reduces parameters by 62% with minimal accuracy loss, enabling efficient edge deployment. The results confirmed that attention-gated continuous-time modeling provides a robust and interpretable approach for large-scale water quality anomaly detection.