Water leakage control in water distribution networks (WDNs) is one of the main challenges of water utilities. The present study proposes a new method to locate a leakage in WDNs using feedforward artificial neural networks (ANNs). For this purpose, two ANNs training cases are considered. For case 1, the ANNs are trained by average daily water demand, including small to large hypothetical leakages. In case 2, the ANNs are trained by hourly water demand and variable hourly nodal leakages over 24 hours. The training parameters are determined by EPANET2.0 hydraulic simulation software using MATLAB programming language. In both cases, first, ANNs are trained using flow rates of total pipes number. Then, sensitivity analysis is performed by hybrid ANNs for the flow rates of pipes number less than the number of the total pipes. The results of proposed hybrid ANNs indicate that if at least the flow rates of 10% of the total pipes are known (using flowmeters), then the leakage locations in both cases can be determined. Despite the complexity of case 2, because of the variations of demand and leakage over the 24-hour period, the proposed method could detect the leakage location with high accuracy.

供水网络 (WDN) 中的漏水控制是供水公司面临的主要挑战之一。本研究提出了一种使用前馈人工神经网络 (ANN) 定位 WDN 中泄漏的新方法。为此，考虑了两个人工神经网络训练案例。对于案例 1，人工神经网络通过平均每日需水量进行训练，包括小到大的假设泄漏。在情况 2 中，人工神经网络通过每小时需水量和 24 小时内可变的每小时节点泄漏进行训练。训练参数由EPANET2.0液压仿真软件采用MATLAB编程语言确定。在这两种情况下，首先，使用总管道数的流量来训练人工神经网络。然后，混合人工神经网络对管道数小于总管道数的流量进行敏感性分析。建议的混合人工神经网络的结果表明，如果至少知道总管道的 10% 的流量（使用流量计），则可以确定两种情况下的泄漏位置。尽管情况 2 很复杂，但由于需求和泄漏在 24 小时内的变化，所提出的方法可以高精度地检测泄漏位置。