Leakages are a major risk in water distribution networks as they cause water loss and increase contamination risks. Leakage detection is a difficult task due to the complex dynamics of water distribution networks. In particular, small leakages are hard to detect. From a machine-learning perspective, leakages can be modeled as concept drift. Thus, a wide variety of drift detection schemes seems to be a suitable choice for detecting leakages. In this work, we explore the potential of model-loss-based and distribution-based drift detection methods to tackle leakage detection. We additionally discuss the issue of temporal dependencies in the data and propose a way to cope with it when applying distribution-based detection. We evaluate different methods systematically for leakages of different sizes and detection times. Additionally, we propose a first drift-detection-based technique for localizing leakages.

翻译：漏损是供水管网中的重大风险，它会导致水资源流失并增加污染风险。由于供水管网动力学的复杂性，漏损检测是一项艰巨的任务，尤其是微小漏损难以被探测。从机器学习角度看，漏损可建模为概念漂移现象，因此多种漂移检测方案似乎适合用于漏损检测。本研究探索了基于模型损失和基于分布的漂移检测方法在漏损检测中的潜力，并讨论了数据中的时序依赖性问题，提出了在应用基于分布检测时应对该问题的方法。我们系统评估了不同方法在不同规模漏损及检测时间下的表现，同时首次提出一种基于漂移检测的漏损定位技术。