NRW Estimation and Localization in Water Distribution Networks via Hydraulic Model Calibration using 24/7 Monitoring Data

Operations of water distribution networks (WDNs) are monitored daily via installed data loggers, where the collated hydraulic data can be leveraged to improve the system’s operations over time, and to minimize total economic losses due to non-revenue water (NRW). In collaboration with Public Utility Board (PUB), Singapore’s National Water Agency, a practically novel model calibration approach using 24/7 monitoring flow and pressure data has been developed to facilitate PUB’s Smart Water Grid (SWG). The approach is developed as a generic integrated solution process to conduct a series of systematic analyses for daily WDN model calibration, namely: (1) estimating the system’s daily NRW contributions; (2) performing flow calibration that involves net demand consumption calibration, adjusting pumps operational configurations and localizing NRW sources when the system’s daily estimated NRW volume exceeds its assumed background volume; (3) performing energy calibration by rectifying possible drifting in monitored pressure head data and calibrating other physical properties which include, but not limited to, pipe roughness and valve settings, especially during peak-demand hours. The effectiveness of our proposed approach is subsequently tested on three WDN zones in Singapore, having a total pipe length of >100km, that comprises of atypical water usage patterns. The results of model calibration for one of three zones is presented in this paper. The key outcomes derived from the study are: (a) localized a reported leakage event by PUB to less than 100m; (b) calibrated the system’s flow balance, to less than 1% average mean absolute percentage error (MAPE), by first identifying and addressing the system’s billing data uncertainties, followed by localizing anomaly events that account for the total NRW volume estimated; and (c) calibrated the system’s pipe roughness values to improve the total energy balance by achieving an average daily MAPE of 4.0%.