Earticle

This paper addresses the critical task of anomaly detection in river network sensor data, essential for accurate and continuous water quality monitoring. We propose M-MAD (Multi-Modal Anomaly Detection), a novel approach that integrates multi-modal features, including sensor data, weather information, and historical anomalies. M-MAD builds on the Graph Deviation Network (GDN) framework by introducing an improved anomaly threshold criterion derived from the learned graph structure. Our evaluation employs rigorous benchmarking simulations that mimic complex dependency structures and diverse anomalies, thoroughly assessing the strengths and weaknesses of M-MAD compared to existing methods. Results demonstrate M-MAD's superior performance in handling high-dimensional datasets and its enhanced interpretability, crucial for effective anomaly detection.