An innovative machine learning based framework for water distribution network leakage detection and localization
An innovative machine learning based framework for water distribution network leakage detection and localization
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DOI：
10.1177/14759217211040269
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Xudong Fan;X. Yu
中科院分区：
文献类型：
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作者：
Xudong Fan;X. Yu
地下水管网(WDN)的泄漏在美国每年浪费超过10亿加仑的水，给我们的社区造成重大的社会经济损失。然而，检测和定位无线数字网络中的泄漏仍然是一个具有挑战性的技术问题，尽管这一领域已经取得了重大进展。机器学习的进展为利用数据驱动的方法识别泄漏提供了新的途径。然而，服务中的WDN在泄漏条件下缺乏标记数据，这使得使用普通的ML模型是不可行的。提出了一种新的基于机器学习的无线传感器网络泄漏检测与定位框架。该框架利用无线传感器网络的拓扑关系及其泄漏特征进行无线传感器网络划分和传感器放置，然后利用监测数据进行泄漏检测和定位。将CTL-SSL框架应用于两个实验WDN，通过使用小于10%的不平衡数据，获得了95%的泄漏检测准确率和约83%的最终泄漏定位准确率。所开发的CTL-SSL框架通过降低对数据的要求、指导传感器的最优布置以及通过WDN泄漏区的划分来定位泄漏，从而改进了泄漏检测策略。它具有出色的可伸缩性、可扩展性和可升级性，可将应用程序升级到各种类型的WDN。它将为世界发展网络的可持续管理提供宝贵的工具。
Leakages in the underground water distribution networks (WDNs) waste over 1 billion gallon of water annually in the US and cause significant socio-economic loss to our communities. However, detecting and localization leakage in a WDN remains a challenging technical problem despite of significant progresses in this domain. The progresses in machine learning (ML) provides new ways to identify the leakage by data-driven methods. However, in-service WDNs are short of labeled data under leaking conditions, which makes it infeasible to use common ML models. This study proposed a novel machine learning (ML)-based framework for WDN leak detection and localization. This new framework, named clustering-then-localization semi-supervised learning (CtL-SSL), uses the topological relationship of WDN and its leakage characteristics for WDN partition and sensors placement, and subsequently utilizes the monitoring data for leakage detection and leakage localization. The CtL-SSL framework is applied to two testbed WDNs and achieves 95% leakage detection accuracy and around 83% final leakage localization accuracy by use of unbalanced data with less than 10% leaking data. The developed CtL-SSL framework advances the leak detection strategy by alleviating the data requirements, guiding optimal sensor placement, and locating leakage via WDN leakage zone partition. It features excellent scalability, extensibility, and upgradeability for applications to various types of WDNs. It will provide valuable a tool in sustainable management of the WDNs.