Shapelet Temporal Evolution Graph Network for Water Quality Anomaly Detection

Water quality anomaly detection refers to the identification of abnormal changes in water parameters, which is crucial for ensuring environmental safety and preventing contamination events. With the growing volume of water environment sensing data and increasing demand for intelligent, transparent water quality management systems, achieving accurate, rapid, and interpretable anomaly detection has become a critical challenge in early warning systems. To tackle this challenge, this work proposes an anomaly detection model named Shapelet Temporal Evolution Graph Network (STEG), which constructs time-aware Shapelets and adopts graph attention networks to build Shapelets evolution graphs, learning multidimensional dynamic relationships within and between time segments. By incorporating both local and global temporal evolution factors, the approach ensures the interpretability of both the detection process and its resulting outputs. Experiments on two real-world datasets show that STEG outperforms state-of-the-art methods in terms of anomaly detection accuracy and generalization. Moreover, it provides clear and transparent reasoning for water quality anomaly detection.