Flow Measurement-Based Self-Adaptive Line Segment Clustering Model for Leakage Detection in Water Distribution Networks

Abstract

Leakage detection of water distribution networks (WDNs) plays a key role in water management. Current leakage detection methods mostly have high requirements for the installation of sensors in WDN. So, there needs an efficient and convenient method when dealing with a new WDN. In this article, a novel, timely, and accurate leakage detection model is proposed, called self-adaptive line segment clustering (SALICT), which only asks for flow measurement data of WDN, reducing the demand for sensors. The proposed method focuses on the identification phase of leakage detection to improve the sensitivity to leakage. To achieve this, SALICT efficiently fuses a density-based clustering analysis method and a line segment clustering model. Furthermore, a series of auxiliary decision functions are introduced and designed to adaptively select parameters, obtain cluster centers, and detect leakage data according to data distribution characteristics. Notably, the proposed method can detect leakages in real-time after learning from historical data. Besides that, the proposed method has the potential to be easily used in different types of WDN. Experimental results show that the proposed method can achieve a high leakage identification ratio and low false alarm ratio and has low time consumption in various water use scenarios.