An Automated Geographical Information System-Based Spatial Machine Learning Method for Leak Detection in Water Distribution Networks (WDNs) Using Monitoring Sensors  
Doha Elshazly  
1 Department of Civil Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates; [EMAIL_ADDRESS] (D.E. ); [EMAIL_ADDRESS] (R.G. ); [EMAIL_ADDRESS] (M.M.M. ); [EMAIL_ADDRESS] (S.A.)  
1 Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates; [EMAIL_ADDRESS] (C.E. ); [EMAIL_ADDRESS] (R.G. ); [EMAIL_ADDRESS] (M.M.M. ); [EMAIL_ADDRESS] (S.A.)  
2 Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates; [EMAIL_ADDRESS] (C.E. ); rgawai  
There have been several studies on water leakage and leak detection techniques. The most commonly used leak detection methods include acoustic, infrared thermography, pressure and flow sensor signals, and ground penetrating radars [2; 3]. These methods are point-based and rely on previous assumptions of leak locations. For this reason, they are limited in their ability to efficiently handle an entire large WDN since most experiments have been conducted on pilot-scale laboratory models. Some concerns still relate to the lack of time efficiency and the necessity of skilled labourers on-site. Moreover, the high dependency on the use of data collected by pressure sensors was proven to be inaccurate because there are several factors affecting pressure readings apart from leakage. WDNs are often built with flow, water quality, and pressure sensors for continuous monitoring. Leaks within the WDN are often accompanied with an instant pressure and flow drop, with a deterioration in water quality as a result [4]. Extensive research has been carried out on the use of pressure drops in WDNs for leak detection due to the wide availability of low-cost sensors and ease of installation [5]. [PERSON] and [PERSON] proposed an integrated leak detection method complemented by an electronic system that aids in the remote transmission of pressure data based on global systems for mobile communication (GSM) protocols [6]. However, the application of this system was limited to small-scale networks and its feasibility on large WDNs was not tested. Another study was conducted to develop an in-pipe leak detection system to spot leaks based on radial pressure gradients. The method was tested on a lab-scale prototype simulating real leaks and was found to accurately identify leaks using pressure readings [7]. Another study used the Head Loss Ratio (HLR) to detect leaks [8]. Although successfully proven theoretically, this study was applied to simulated small WDNs in EPANET Version 3 software. Hence, the method's suitability is still questionable in large, real WDNs [8]. Another study explored the angle method, which was compared with the least-squares optimization and the correlation methods [9]. The method was purely theoretical and mathematical in its formulation and eventually applied to a model Hanoi network. It was found to be 75% efficient in locating leaks using pressure data. However, its applicability in real, large WDNs is questionable as the extent of uncertainty in a large WDN cannot always be mathematically formulated with the approach of this study. A statistical classifier model was derived by another study while considering pressure irregularities in the network to improve the accuracy of the leak localization [10]. The proposed method was further tested by the Hanoi network benchmark. This method was compared with the angle method and was found to be 80% accurate compared to the 50% accuracy of the angle method [10]. Similar to other studies, no attempt was made to investigate the applicability in a large WDN. A state wireless pressure detection sensor was developed to identify leaks based on the measurement of relative indirect pressure changes in plastic pipes [11]. This method was capable of performing continuous monitoring without operator intervention.