However, the applicability in large WDNs is still unexplored. Similarly, [PERSON] and [PERSON] examined the feasibility of using nodal pressure data to locate leaks by extracting several pipes and determining the nodal pressure patterns across the lines [12]. The technique was not sensitive to the size of simulated leak discharge, even though it was found efficient. EPANET was used to investigate leak detection formulations to locate leak positions and their sizes with the help of pressure readings at each pipe node [13]. The average detection accuracy was 82.45% for k-nearest neighbours (KNNs), 66.31% for the naive Bayes network, and 48.90% for decision tree networks. A submersible optics-based pressure sensor was proposed to identify, spot, and quantify leaks based on hydrostatic and pressure transient responses [14]. However, the sensitivity of this sensor was found to be lower than those of traditional sensors. This study mostly focused on the sensor development, not necessarily for a large WDN. Using anomaly detection algorithms, pipe failures were successfully detected based on data obtained from external pressure and temperature sensors [15]. However, it was a point-based leak detection system. Another study on the point-based leak detection system was investigated and successfully used the pressure residual vector method to locate the leaks, taking into account the pipe material and external factors like soil load [16]. The technology was tested in a hypothetical model in EPANET. A study investigated the applicability of sum of squared errors (SSE) optimization functions and successfully detected leaks with various scenarios of leak sizes using simulated network pressure [17]. Another study used sensitivity analysis integrated with mass and energy conservation equations to detect the variations in pressure and flow data in the network in order to aid leak localization [18]. This methodology was capable of detecting water leaks of up to 30 L/min [18]. A different methodology that utilizes pressure data was successfully accompanied by an iForest algorithm to obtain accurate results [5]. The approach was investigated theoretically in a small network in EPANET. The Value of Information (VOI) and Transinformation Entropy (TE) methods, in concert with an optimization model, were able to identify leaks using pressure readings, as well as localize pressure sensors for more accurate results [19]. Another study utilized pattern recognition and the clustering of transient pressure signals to train Artificial Neural Networks (ANNs) for leak localization and estimation. It was found to be successful for real WDNs [20]. Similarly, a real-time detection method could detect pressure irregularities within the network by the use of an unsupervised learning anomaly detection method with an accuracy of 92% and 94% for 2013 and 2017 datasets, respectively [21]. [PERSON] et al. employed a temporal-based leak detection method with the help of multiple pressure sensor placements. They investigated the difference between real-time and predicted model data in order to enhance the detection capability [22]. [PERSON] et al. used machine learning methods including linear discriminant analysis (LDA) and neural networks (NNETs) in integration with Bayesian temporal reasoning with results reaching up to 80% accuracy [23]. A similar study employed the random forest algorithm and performed model training and testing with the help of available pressure data with 96% accuracy [24]. [PERSON] et al. successfully applied the use of pressure reduction valves (PRVs) to monitor and detect leaks with the help of an existing supervisory control and data acquisition (SCADA) system, GIS, and customer information system (CIS) databases [25]. Few studies focused on the flow sensor data to determine the leak locations within a network. [PERSON] et al. employed a linear programming algorithm based on network hydraulics to identify the leak spots using sparse flow data [26].