Water Distribution System Leakage Detection andLocalization Using Water Demand Automated MeterReading: An Underexplored and Advantageous Technique

Abstract

Leakage Detection and Localization (LDL) is one of the greatest challenges to achieving sustainable management in Water Distribution Systems (WDS). As an essential element for the evaluation of LDL approaches, we recently developed LeakG3PD, a simulated dataset that represents WDS behavior in various leakage scenarios, providing all variables and network structure data. Unlike most works in the literature, this article explores the feasibility and advantages of using water demand Automated Meter Reading (AMR) devices, rather than pressure sensors, as the main data source for LDL. To evaluate such an approach, a derived dataset is generated from LeakG3PD using WDS inlet flows and pressures, along with, mainly, consumption demand data. As an introductory work, intuitive and transparent algorithms are applied for LDL. Calculated loss demand is used for detection based on historical thresholds and localization output consists of an ordered list of the pipes in which the insertion of the estimated leak demand causes minimum flow residuals in WDS sources (reservoirs and tanks). A one-step hydraulic simulation is performed to determine the residuals for each candidate pipe. The main difficulties are discussed to provide future improvements. Even though the fundamental step of model calibration is not executed, results indicate promising localization performance for future works. The derived dataset LeakG3PD-OD and the LDL Python programs are made available online for the academic community to contribute with more sophisticated algorithms