However, this study identified the ability to handle large datasets at once, being an advantage. Another real-time leak detection technique using the traditional flow balance integrated with the minimum night flow (MNF) approach was used to provide flow thresholds for accurate leak identification. This method allowed the reduction in future leaks by 36% [27]. A similar study used the computed leakage rate, components, and reduction potential based on MNF to reduce leak frequencies [28]. Another detection methodology utilized nonlinear partial differential equations with the help of flow data for modelling and irregularities detection within the network for leak localization [29]. The method was tested on a pilot-scale model and provided reasonable leak location estimations. Furthermore, another study performed extensive simulations using optimization models that use available flow sensor data to locate possible leaks [30]. It was observed that the majority of the studies using flow data presented low accuracy. Several studies have explored the feasibility of utilizing the pressure and flow meter data to detect leaks. Nevertheless, accurate identification of the leak locations can be challenging when relying solely on separate parameters (flow/pressure) due to the presence of fluctuations along the network length caused by other factors. Additionally, research on combining those parameters in addition to water quality parameters is still in its infancy. The use of machine learning techniques for identifying leak locations using these parameters is nonexistent. There is an apparent necessity to integrate this innovative pressure, flow, and chlorine residual with machine learning-based techniques to improve the accuracy of leak detection and avoid false positive scenarios. Machine learning models that are most suitable for anomaly detection tasks have been explored for this paper. [PERSON] et al. [31] performed a comparative study on different anomaly detection models on the LeakDB dataset, a benchmark dataset for leak detection. After comparing model performances, the authors concluded that proximity-based models performed best on the LeakDB dataset, with the Local Outlier Factor (LOF) model achieving a high entropy weight TOPSIS value of 0.9943. This approach, however, is semi-supervised and does not consider the fact that data collected from WDNs are unlabelled. Another study by [PERSON] et al. [32] reviewed the use of different LOF algorithms for big data streams, showcasing the model's efficiency in various applications including intrusion detection, fraud detection, and medical applications. [PERSON] and [PERSON] [33] used two anomaly detection models for leak detection in compressed air systems, the LOF and Autoencoders. Despite the complexity of the Autoencoder, the LOF was able to perform better with an Area Under Curve (AUC) value of 0.94. This study, however, used synthetic data to test their algorithms due to the lack of available real data in their domain. Along with the LOF, Geographically Weighted Regression (GWR) is a machine learning technique that we found suitable for leak detection in WDNs [34]. GWR has been used in this study with the dataset provided by the Sharjah Electricity and Water Authority (SEWA) to (a) integrate the three leakage predictors (flow, pressure, and chlorine readings in the WDN) given their spatial variability throughout the WDN, and (b) to automate the leak detection workflow. This approach is particularly relevant for large WDNs. To correctly use a GWR model, it is recommended to first test the ordinary least squares (OLS) linear regression to check if the assumptions are met. GWR first requires testing if the residual distribution is not spatially autocorrelated [35; 36], and then to utilize the OLS predictors to build a GWR model [37; 38]. This model was used in this study to help with the workflow automation process and improve the accuracy of predicting leak areas using the sensor recordings.