<![CDATA[Water Distribution Networks (WDNs) suffer substantial water losses due to pipeline leaks, resulting in economic ramifications and exacerbating global water scarcity concerns. This paper seeks to improve the precision of leak detection and the identification of leak locations within WDNs. The pervasive issue of leaks in WDNs poses significant challenges with economic and environmental implications for water utilities. Traditional leak detection methods are time-consuming, resource-intensive, and susceptible to inaccuracies and false alarms due to the random placement of sensors. The detection of concealed background leaks, invisible to the naked eye and situated beneath the surface, presents a particular challenge. This situation complicates efforts for their real-time identification and subsequent repairs. To address these challenges, this paper introduces the SVM-CNN-GT algorithm, an advanced ensemble supervised Machine Learning (ML) approach that incorporates Support Vector Machines (SVM), Convolutional Neural Network (CNN), and Graph Theory (GT). By combining multiple ML algorithms, the SVM-CNN-GT model takes into account various factors that influence leak detection and localization, resulting in more precise and reliable assessments of leak presence and location. The algorithm leverages automatic feature extraction and heterogeneous dual classifiers to accurately assess leaks based on data related to flow rate, pressure, and temperature. Furthermore, a combination probability scheme enhances leak detection efficiency by integrating diverse classifier models with distinct prediction outputs. Through the EPANET performance evaluations, the SVM-CNN-GT algorithm outperforms CNN and SVM algorithms, demonstrating remarkable proficiency with the highest average leak detection accuracy of 98%, followed by CNN at 82% and SVM at 78%.]]>
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