New Bayesian and deep learning spatio-temporal models can reveal anomalies in sensor data more effectively

Environmental and water quality monitoring increasingly relies on high-frequency data streams from sensor networks, yet anomalies in these datasets can compromise their reliability. Here we introduce two novel unsupervised methods for anomaly detection in spatio-temporal sensor arrays specifically designed for highly structured datasets such as those obtained by networks of sensors in rivers. The first is a dynamic Bayesian spatio-temporal model using a reduced rank Gaussian process, and the second is a deep learning architecture called Spatio-Temporal Attention-based LSTM for River Networks. We rigorously evaluate both methods through comprehensive simulation benchmarks incorporating diverse anomaly types common in environmental data. Our comparative analysis reveals the strengths and limitations of each approach, demonstrating superior performance over existing methods in both accuracy and computational efficiency. We further introduce an ensemble method that synergistically combines the strengths of both approaches. Our framework addresses the growing need for robust, efficient algorithms and computational methods for monitoring complex ecosystems, advancing spatio-temporal anomaly detection in environmental applications. By providing detailed implementation guidelines and open-source code, we enable immediate application by ecological and environmental scientists and practitioners, facilitating improved monitoring and enhanced decision-making in river network management.