2025/11/10 by Daniele Ugo Leonzio, Paolo Bestagini, Leonzio, Daniele Ugo +5
Computer Science · Engineering · Environmental Science · #Artificial Intelligence (cs.AI) #Energy Efficient Wireless Sensor Networks #FOS: Computer and information sciences #Machine Learning (cs.LG) #Water Quality Monitoring Technologies #Water Systems and Optimization
paper · pdf · doi:10.48550/arxiv.2511.11650
openalex publication_date 2025/11/10 · openalex created_date 2025/11/19 · openalex updated_date 2026/07/28
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.