Abstract
Water distribution systems are critical infrastructures, and are highly susceptible to a wide range of anomalies like leaks and component failures. Hence, timely detection of abnormal system behavior is essential for their safe and efficient operation. To address this challenge, we generated synthetic hydraulic datasets to train a machine learning tool, tailored for anomaly detection and classification tasks. The proposed architecture integrated bidirectional gated recurrent unit layers with time-distributed dense layers employing Rectified Linear Unit activations, enabling the extraction of temporal dependencies alongside spatial feature representations. The strong performance achieved highlights the robustness of the approach in distinguishing between normal operating states and heterogeneous anomaly classes, demonstrating its potential for enhancing system reliability.