Anomaly Localization by Applying Data-Driven Analysis and Parallel Optimization of Hydraulic Model Calibration

This paper presents an integrated approach using both data-driven and hydraulic model-based methods to localize anomaly events in near real time (NRT). Upon detecting an NRT anomaly event, the pressure drops at sensor locations are calculated, followed by estimating the pressure drops at junction nodes via an inverse-distance weighted interpolation method. Clustering is then performed based on pressure drops at junction nodes and network topology to segregate and reduce the search areas. Afterwards, a genetic algorithm optimization is performed with hydraulic model simulations to further pinpoint the anomaly hotspots. The integrated method has been tested on real leakage events with field data, where the localized leak hotspots are within 300 m of the ground-truth leaks.