The water supply network (WSN) is subjected to leaks that compromise its service to the communities, which, however, is challenging to identify with conventional approaches before the consequences surface. This study developed Machine Learning (ML) models to detect leaks in the WDN. Water pressure data under leaking versus non-leaking conditions were generated with holistic WSN simulation code EPANET considering factors such as the fluctuating user demands, data noise, and the extent of leaks, etc. The results indicate that Artificial Neural Network (ANN), a supervised ML model, can accurately classify leaking versus non-leaking conditions; it, however, requires balanced dataset under both leaking and non-leaking conditions, which is difficult for a real WSN that mostly operate under normal service condition. Autoencoder neural network (AE), an unsupervised ML model, is further developed to detect leak with unbalanced data. The results show AE ML model achieved high accuracy when leaks occur in pipes inside the sensor monitoring area, while the accuracy is compromised otherwise. This observation will provide guidelines to deploy monitoring sensors to cover the desired monitoring area. A novel strategy is proposed based on multiple independent detection attempts to further increase the reliability of leak detection by the AE and is found to significantly reduce the probability of false alarm. The trained AE model and leak detection strategy is further tested on a testbed WSN and achieved promising results. The ML model and leak detection strategy can be readily deployed for in-service WSNs using data obtained with internet-of-things (IoTs) technologies such as smart meters.

中文翻译：

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机器学习模型和策略，用于快速，准确地检测供水网络中的泄漏

供水网络（WSN）遭受泄漏，损害了其对社区的服务，但是，在后果浮出水面之前，很难用常规方法进行识别。这项研究开发了机器学习（ML）模型来检测WDN中的泄漏。考虑到诸如用户需求波动，数据噪声和泄漏程度等因素，使用整体WSN模拟代码EPANET生成泄漏与非泄漏条件下的水压数据。结果表明，人工神经网络（ANN）监督的机器学习模型，可以准确地将泄漏情况与非泄漏情况进行分类；但是，它在泄漏和非泄漏条件下都需要平衡的数据集，这对于大多数在正常服务条件下运行的真实WSN来说是困难的。自动编码器神经网络（AE），进一步开发了无监督的ML模型，以检测不平衡数据的泄漏。结果表明，当传感器监控区域内的管道中发生泄漏时，AE ML模型可达到较高的精度，否则会降低精度。该观察将为部署监视传感器以覆盖所需监视区域提供指导。提出了一种基于多次独立检测尝试的新策略，以进一步提高AE进行泄漏检测的可靠性，并发现该策略可大大降低错误警报的可能性。训练有素的AE模型和泄漏检测策略在测试平台WSN上进行了进一步测试，并获得了可喜的结果。使用物联网（IoT）技术（例如智能电表）获得的数据，可以轻松地将ML模型和泄漏检测策略部署到服务中的WSN中。