A significant percentage of treated water is lost due to leakage in water distribution systems. The state-of-the-art leak detection and localization schemes use a hybrid approach to hydraulic modeling and data-driven techniques. Most of these works, however, focus on single leakage detection and localization. In this research, we propose to use combined pressure and flow residual data to detect and localize multiple leaks. The proposed approach has two phases: detection and localization. The detection phase uses the combination of pressure and flow residuals to build a hydraulic model and classification algorithm to identify leaks. The localization phase analyzes the pattern of isolated leak residuals to localize multiple leaks. To evaluate the performance of the proposed approach, we conducted experiments using Hanoi Water Network benchmark and a dataset produced based on LeakDB benchmark's dataset preparation procedure. The result for a well-calibrated hydraulic model shows that leak detection is 100% accurate while localization is 90% accurate, thereby outperforming minimum night flow and raw- and residual-based methods in localizing leaks. The proposed approach performed relatively well with the introduction of demand and noise uncertainty. The proposed localization approach is also able to locate two to four leaks that existed simultaneously.

由于配水系统泄漏，很大一部分处理过的水会流失。最先进的泄漏检测和定位方案使用混合方法进行液压建模和数据驱动技术。然而，这些工作中的大多数都集中在单个泄漏检测和定位上。在这项研究中，我们建议使用组合的压力和流量残余数据来检测和定位多个泄漏。所提出的方法有两个阶段：检测和定位。检测阶段使用压力和流量残差的组合来构建水力模型和分类算法来识别泄漏。定位阶段分析孤立泄漏残留的模式以定位多个泄漏。为了评估所提出方法的性能，我们使用河内水网基准和基于 LeakDB 基准的数据集准备程序生成的数据集进行了实验。校准良好的水力模型的结果表明，泄漏检测的准确度为 100%，而定位的准确度为 90%，因此在定位泄漏方面优于最小夜间流量和基于原始和残留的方法。由于引入了需求和噪声不确定性，所提出的方法表现相对较好。提议的定位方法还能够定位同时存在的两到四个泄漏。从而在定位泄漏方面优于最小夜间流量和基于原始和残留的方法。由于引入了需求和噪声不确定性，所提出的方法表现相对较好。提议的定位方法还能够定位同时存在的两到四个泄漏。从而在定位泄漏方面优于最小夜间流量和基于原始和残留的方法。由于引入了需求和噪声不确定性，所提出的方法表现相对较好。提议的定位方法还能够定位同时存在的两到四个泄漏。