A multi-point leakage prediction statistical method using Bayesian inference in water distribution networks

Abstract

ABSTRACT Leakage in water distribution networks (WDNs) not only leads to serious water loss and pipe contamination but also affects residents’ daily water. Accurate localization of leaks in WDN is significant to conserve water resources and reduce economic losses. However, in traditional optimization and verification methods for leak detection, factors such as modeling and pressure monitoring point errors are not taken into account, resulting in the deviation from the actual location of leaks. To address the mentioned issues, this article presents a WDN leakage probability prediction method based on Bayesian inference. The method converts the prior probability of leakage events into posterior probability. Then, by utilizing the posterior probability density function, the uncertainty of modeling and measurement errors in the hydraulic simulation model are quantified. The probability distribution of leakage pipes and leakage quantity within the leakage area is calculated, allowing for the location prediction and corresponding magnitude of leakage. The experimental results indicate that the prediction model can detect unknown quantities of leakage events. By collecting multiple sets of leakage data, it is possible to accurately predict the location and quantity of leaks, enhancing the efficiency of leakage detection in large-scale water supply networks and providing decision-making assistance for water utilities.