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.

水分配网络中的泄漏是一项重大风险，会导致水损失和增加污染风险。本文探讨了基于模型损失和基于分布的漂移检测方法在泄漏检测中的潜力，还讨论了数据中的时间依赖问题并提出了解决办法。我们对不同大小和检测时间的泄漏进行了系统评估，此外，我们提出了一种基于漂移检测的泄漏定位技术。