Stochastic model-based leakage prediction in water mains considering pipe condition uncertainties

Abstract

Energy loss through leakage in water distribution networks (WDNs) has been estimated to add up to 20% of the freshwater supply in the US. One common practice to battle non-revenue water in WDNs is proactively pinpointing leak sources through both conventional and novel, data-driven methods. Conventional, in-practice leakage detection approaches suffer from costliness and laboriousness, and thus novel model-based techniques are becoming a robust, accurate, and cost-effective alternative. One of the existing limitations in the literature regarding novel leak localization methods and thus the novelty in this study is factoring in the stochastic behavior of water pipe conditions while predicting leakage through consumption and cyber-monitoring data. Hence, this paper presents a direct, data-driven, artificially intelligent predictive tool for leak localization and severity measurement. In this leakage prediction model, uncertain pipe conditions such as pipe diameters and roughness coefficients are stochastically approximated in a Monte Carlo simulations framework using water quality-included empirical formulation. A novel accuracy index is also developed to measure the precision of leak localization by employing a graph connectivity algorithm. Prediction results and sensitivity analyses on a large WDN benchmark demonstrate that the proposed stochastic leakage prediction model offers robustness in locating leak sources and measuring severity in various leak test cases. Not only does this research offer new numerical insights into the probabilistic nature of water pipelines’ dynamic parameters amid leakage, it also pushes the boundary of smart leak detection approaches at water utilities by offering cost-effective and data-driven horizons.