用压力数据和异常检测，精准识别供水管网漏水。
Enhanced Water Leak Detection with Convolutional Neural Networks and One-Class Support Vector Machine
- 基于管网拓扑与无泄漏压力数据，构建特征提取+单类SVM的异常检测模型。
- 在模拟的摩德纳管网数据上，漏损检测准确率优于现有方法。
- 适合城市供水系统运维人员、智慧水务研究者参考使用。
水是关键资源，需高效管理。每年大量水资源因供水管网（WDN）泄漏而损失，亟需可靠有效的漏损检测与定位系统。近年来，数据驱动方法因性能优越日益受到关注。本文提出一种新型漏损检测方法，基于管网中多个节点的压力测量数据。该方法为完全数据驱动，仅依赖管网拓扑结构及无泄漏状态下的压力数据采集。通过特征提取器与仅在无泄漏数据上训练的单类支持向量机（One-Class SVM），将漏损视为异常进行检测。在模拟的摩德纳管网（Modena WDN）数据集上的实验表明，该方法优于近期其他漏损检测技术。
原文摘要 · Abstract (English)
Water is a critical resource that must be managed efficiently. However, a substantial amount of water is lost each year due to leaks in Water Distribution Networks (WDNs). This underscores the need for reliable and effective leak detection and localization systems. In recent years, various solutions have been proposed, with data-driven approaches gaining increasing attention due to their superior performance. In this paper, we propose a new method for leak detection. The method is based on water pressure measurements acquired at a series of nodes of a WDN. Our technique is a fully data-driven solution that makes only use of the knowledge of the WDN topology, and a series of pressure data acquisitions obtained in absence of leaks. The proposed solution is based on an feature extractor and a one-class Support Vector Machines (SVM) trained on no-leak data, so that leaks are detected as anomalies. The results achieved on a simulate dataset using the Modena WDN demonstrate that the proposed solution outperforms recent methods for leak detection.
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