# A Multi-Leak Identification Scheme Using Multi-Classification for Water Distribution Infrastructure
## 1 Introduction  
Freshwater is one of the most important components to maintain human survival. The reliability and sustainability of water distribution infrastructure (WDI) are always the fundamental issues determining the livability of cities. The WDIs established in the past are now facing an aging problem. The number of leakages and bursts has been ever-escalating year-by-year. This results in not only economic losses but also the dissipation of natural resources. Half of the freshwater is wasted worldwide due to leaking pipelines [1]. In the United States, over 200,000 water bursts are recorded, yielding USD 2 billion of economic loss every year [2]. In Europe, WDIs in England, France, and Italy are leaking around 25% of freshwater annually [2]. It is also highlighted that 15% of the freshwater is wasted due to leaking pipelines in Hong Kong [3]. To combat this problem, current research on water leak detection technologies with high detection accuracy shows great effectiveness in protecting water resources and mitigating economic loss. A variety of water leak detection approaches for WDIs were developed by researchers. One of the most common leak detection methods is manual listening through portable detection devices [4]. These devices are usually placed on the surface of the pipelines, and the leaking point is determined by listening for changes in sound or vibration produced by leaky pipes. Paper [5] proposed an effective detection scheme based on acoustic emissionand pattern recognition. Paper [6] proved the leak detection efficiency via laboratory validation experiments with ground penetrating radar. Other detection approaches based on portable devices have been developed [7; 8; 9]. However, low penetration capability and ambient noise are two critical challenges for external portable detection. Moreover, lack of autonomy is another disadvantage for external portable detection. As a result, internal automatic leak detection methods are being explored. An automatic leak detection system integrates internal-sensor water network and automatic leak detection algorithms, which enable capacities in real-time motoring and early detection. In an internal-sensor water network, wireless sensors (e.g., flow meters, pressure sensors) are embedded into pipelines for remote data measurement and collection. Based on the data from internal sensors in the water distribution network, automatic leak detection algorithms have been developed to analyze variations in hydraulic behavior, indicate pipe status, and produce early alarms. Given their high efficiency, internal automatic leak detection systems have been reported extensively in the literature [10; 11; 12; 13]. The paper [11] proposed a small-town water system combining water balance and minimum night flow approaches to detect water leakage efficiently. However, only the influence on water flow was considered. The detection accuracy can be improved by integrating other types of sensors (e.g., pressure sensors). In paper [13], an accelerometer-based automatic leak detection system was developed for early detection of single-event leaks in water pipelines with high accuracy; however, it shows relative low efficiency in the detection of multiple leak. To reduce the computational complexity, in [14] a model reduced by integrating multiple pipe branches into a single node was proposed to convert complicated WDI into a network. In terms of the practicability of internal automatic leak detection, various automatic leak detection algorithms have been developed, including state estimation algorithms [15; 16], signal analysis algorithms [2; 17], machine learning algorithms [12; 18; 19], etc. The paper [15] proposed a burst detection approach that utilizes an adaptive Kalman filter to perform hydraulic measurement of flow and pressure in a district metered area (DMA). In research [17], a burst detection scheme using principal component analysis was proposed, which enables a sensitive and quick analysis of water flow in DMAs with low computational complexity.