Facing climate change the already limited availability of drinking water will decrease in the future rendering drinking water an increasingly scarce resource. Considerable amounts of it are lost through leakages in water transportation and distribution networks. Leakage detection and localization are challenging problems due to the complex interactions and changing demands in water distribution networks. Especially small leakages are hard to pinpoint yet their localization is vital to avoid water loss over long periods of time. While there exist different approaches to solving the tasks of leakage detection and localization, they are relying on various information about the system, e.g. real-time demand measurements and the precise network topology, which is an unrealistic assumption in many real-world scenarios. In contrast, this work attempts leakage localization using pressure measurements only. For this purpose, first, leakages in the water distribution network are modeled employing Bayesian networks, and the system dynamics are analyzed. We then show how the problem is connected to and can be considered through the lens of concept drift. In particular, we argue that model-based explanations of concept drift are a promising tool for localizing leakages given limited information about the network. The methodology is experimentally evaluated using realistic benchmark scenarios.

翻译：面对气候变化，原本就已有限的饮用水资源在未来将进一步减少，使饮用水成为一种日益稀缺的资源。大量的饮用水在输水和配水网络的泄漏中损失殆尽。由于配水管网中复杂的相互作用和不断变化的需求，泄漏检测与定位是一项极具挑战性的问题。尤其是小泄漏难以精确定位，然而对其定位对于避免长期水量损失至关重要。尽管存在多种解决泄漏检测与定位任务的方法，但这些方法依赖于系统的各类信息，例如实时需求测量值和精确的网络拓扑结构，而这在许多实际场景中是不切实际的假设。相比之下，本工作尝试仅利用压力测量值进行泄漏定位。为此，首先采用贝叶斯网络对配水管网中的泄漏进行建模，并分析系统动态特性。随后，我们展示了该问题如何与概念漂移相关联，并可通过概念漂移的视角加以审视。具体而言，我们认为在关于网络信息有限的情况下，基于模型的概念漂移解释方法是定位泄漏的一种有前景的工具。该方法的有效性通过逼真的基准场景进行了实验评估。