Abstract
The continuous increase of water distribution networks (WDNs) in size and complexity poses significant management challenges, including a high risk of failures. Due to the intrinsic interconnected feature of water flow, including losses, this study proposes a methodology based on graph correlation and multilayer network analysis for leak detection and localization in WDNs with multiple components (infrastructure, control devices, hydraulic sensors). The detection process involves correlating monitored data to create a temporal graph and classify vertices. The classification values are then analyzed by the z-score and interquartile range algorithms to detect anomalies. The localization process uses a multi-graph approach that combines sensor data and network topology to determine the sensor coverage area. The Dynamic Time Warping algorithm calculates the similarity between monitored and simulated leak data, identifying likely leak locations. The results demonstrate the methodology's effectiveness, detecting anomalies 15 minutes after the start of the leak and locating them within a 50-meter range from the actual location of the leak. Furthermore, the research highlights the advantages of using a method based on multilayer networks, which offers insights into leak location, sensor coverage, and reduction of the network's sample space. Furthermore, the approach presents a proposal to reduce exhaustive hydraulic simulations.