With increasing adoption of advanced meter infrastructure, smart sensors together with SCADA (Supervisory Control and Data Acquisition) systems, it is imperative to develop novel data analytics and couple the results with hydraulic modeling to improve the quality and efficiency of water services. One important task is to timely detect and localize anomaly events, which may include, but not be limited to, pipe bursts and unauthorized water usages. In this paper, a comprehensive solution framework has been developed for anomaly detection and localization by formulating and integrating data-driven analytics with hydraulic model calibration. Data analysis for anomaly detection proceeds in multiple steps including the following: (1) data pre-processing to eliminate and correct erroneous data records, (2) outlier detection by statistical process control methods and deep machine learning, and (3) system anomaly classification by correlation analysis of multiple sensor events. Classified system anomaly events are subsequently localized via hydraulic model calibration. The integrated solution framework is developed as a user-friendly and effective software tool, tested, and validated on the selected target areas in Singapore.

Due to the increasing deployment of sensors in water distribution networks (WDNs) for continuous monitoring, hydrodynamic data are readily available for engineers to improve the daily operations of WDNs. In collaboration with Public Utility Board (PUB), Singapore’s National Water Agency, an alternative model calibration approach using continuously monitored data is proposed to facilitate PUB’s smart water grid (SWG). The generic approach was developed as an integrated solution methodology for practical engineers to conduct a series of systematic analyses daily, namely, (1) estimating the system’s daily nonrevenue water (NRW) volume and NRW time series; (2) adjusting the available pumps’ operational curves and control statuses, followed by calibrating the system’s net demand pattern to fulfill the flow balance accounting for the actual consumption; (3) identifying and rectifying possible offsets in the monitored pressure head values for each sensor station; (4) performing model calibration with anomaly localization analysis when the system’s total NRW volume exceeds its assumed background NRW volume; and (5) calibrating other physical properties to fulfill the system’s energy balance, especially for the peak demand hours. The effectiveness of our proposed approach was then tested and verified using a real-world WDN having total pipe length of $>650\mathrm{km}$ with available monitoring data. Key findings from the case study analysis include (1) anomaly events localized including, but not limited to, five out of six reported leaks for the selected week to within 400 m with lead time of 1–2 days; (2) the system’s initial flow imbalance addressed by estimating the daily NRW volume and localizing the possible anomaly events; and (3) pipe roughness values calibrated to further improve the energy balance in the system, especially during the peak demand hours, by attaining an average mean absolute percentage error (MAPE) score of 2.5%.