# GIS-Based Identification of Locations in Water Distribution Networks Vulnerable to Leakage
###### Abstract
GIS also reduces costs and increases operational efficiency, especially in managing infrastructures such as highways, roads, WDNs, and pipelines. However, a GIS-based approach requires high technical knowledge to ensure accurate data interpretation, consisting of multiple layers for a complex task such as leakage detection. GIS-based methods must adequately manage the data quality to develop advanced prediction queries on the status of the water pipeline. This solution requires adaptable and flexible data aligned with the unique characteristics of the WDN. A flexible GIS-based detection system enhances information interoperability from different sources. In addition to utilizing it to represent the WDN layers (refer to Figure 1), GIS was used in this study to determine locations in the WDNs that are vulnerable to leakage by combining six leakage-conditioning factors using the logistic regression and vulnerability analysis. It was also used for creating leakage vulnerability maps. Logistic regression and vulnerability analysis is utilized to identify the parts of WDNs that are susceptible to leaks. Logistic regression is a form of predictive analysis that describes relationships between variables in different forms, such as nominal, ordinal, and  
Figure 1: Studied DMAs. ratio scales. The model coefficients relating to the dependent variable are estimated using the experience of the user or maximum likelihood estimation (MLE) [17; 18]. Several studies have been conducted to compare the performance of various leak detection models and approaches, using different statistical and machine learning models for predicting pipeline failure in WDNs [19; 20]. One study enhanced the understanding of leak detection prediction by evaluating the accuracy and performance of modeling approaches such as linear regression, Poisson regression, and polynomial regression [19]. Another study highlighted that accurate models must consider static factors and time-dependent variables [8]. Models that rely on static elements such as pipe material and soil type could potentially lead to biased predictions. Including fixed and varying factors should enhance the accuracy of leak detection predictions. For instance, considering time-dependent factors allows prediction models to classify pipes based on expected deterioration and other stress elements. The current state of the art highlights the importance of transient phenomena in managing WDNs. According to one study [21], transient flows form distribution systems during transient flow states. This phenomenon results from planned or unplanned events in the system, which change pipe flow parameters. It was noted that considering transient characteristics is essential for improving leak detection [21]. Two different studies stated that pipe anomalies are important to consider, since they result in flow capacity reductions and energy losses within the distribution network [21; 22]. The novelty of this manuscript is to create a vulnerability map which is a graphical representation or visualization of areas or systems that are at risk of being compromised or exploited by potential threats or attacks. These maps can be used to identify areas of weakness in a system or network and prioritize efforts to strengthen security measures. Vulnerability analysis is therefore a systematic process of identifying, classifying, evaluating, and assessing susceptibility to hazards [23]. Vulnerability analysis is crucial for effective planning to mitigate the impact of risks. There are various approaches to identifying different types of vulnerability. The most common vulnerability assessment requires the calculation of Vulnerability Indices (VI) using other parameters that are considered indicators of vulnerability [24]. Calculations of vulnerability indices utilize parameters consisting of potentially influential variables that are given a score depending on their effect on decreasing or increasing vulnerability [24; 25].