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。

探讨概念漂移检测在水配供网络泄漏检测中的适用性

Investigating the Suitability of Concept Drift Detection for Detecting  Leakages in Water Distribution Networks

Leakages in drinking water distribution networks pose significant challenges
to water utilities, leading to infrastructure failure, operational disruptions,
environmental hazards, property damage, and economic losses. The timely
identification and accurate localisation of such leakages is paramount for
utilities to mitigate these unwanted effects. However, implementation of
algorithms for leakage detection is limited in practice by requirements of
either hydraulic models or large amounts of training data. Physics-informed
machine learning can utilise hydraulic information thereby circumventing both
limitations. In this work, we present a physics-informed machine learning
algorithm that analyses pressure data and therefrom estimates unknown irregular
water demands via a fully connected neural network, ultimately leveraging the
Bernoulli equation and effectively linearising the leakage detection problem.
Our algorithm is tested on data from the L-Town benchmark network, and results
indicate a good capability for estimating most irregular demands, with R2
larger than 0.8. Identification results for leakages under the presence of
irregular demands could be improved by a factor of 5.3 for abrupt leaks and a
factor of 3.0 for incipient leaks when compared the results disregarding
irregular demands.

该研究论文介绍了一种利用物理知识驱动的机器学习算法，通过分析压力数据来估计未知的水需求，最终利用伯努利方程并线性化泄漏检测问题。该算法在 L-Town 基准网络的数据上进行了测试，结果显示其在估计大多数不规则需求方面具有较好的能力，R2 大于 0.8；与不考虑不规则需求的结果相比，对于突发泄漏和潜在泄漏情况下的泄漏识别结果分别提高了 5.3 倍和 3.0 倍。