# GIS-Based Identification of Locations in Water Distribution Networks Vulnerable to Leakage
###### Abstract  
The detection of leakages in Water Distribution Networks (WDNs) is usually challenging and identifying their locations may take a long time. Current water leak detection methods such as model-based and measurement-based approaches face significant limitations that impact response times, resource requirements, accuracy, and location identification. This paper presents a method for determining locations in the WDNs that are vulnerable to leakage by combining six leakage-conditioning factors using logistic regression and vulnerability analysis. The proposed model considered three fixed physical factors (pipe length per junction, number of fittings per length, and pipe friction factor) and three varying operational aspects (drop in pressure, decrease in flow, and variations in chlorine levels). The model performance was validated using 13 district metered areas (DMAs) of the Sharjah Electricity and Water Authority (SEWA) WDN using ArcGIS. Each of the six conditioning factors was assigned a weight that reflects its contribution to leakage in the WDNs based on the Analytic Hierarchy Process (AHP) method. The highest weight was set to 0.25 for both pressure and flow, while 0.2 and 0.14 were set for the chlorine and number of fittings per length, respectively. The minimum weight was set to 0.08 for both length per junction and friction factor. When the model runs, it produces vulnerability to leakage maps, which indicate the DMAs' vulnerability classes ranging from very high to very low. Real-world data and different scenarios were used to validate the method, and the areas vulnerable to leakage were successfully identified based on fixed physical and varying operational factors. This vulnerability map will provide a comprehensive understanding of the risks facing a system and help stakeholders develop and implement strategies to mitigate the leakage. Therefore, water utility companies can employ this method for corrective maintenance activities and daily operations. The proposed approach can offer a valuable tool for reducing water production costs and increasing the efficiency of WDN. logistic regression; vulnerability analysis; water distribution network; analytic hierarchy process; leakage detection; water losses +
Footnote †: journal: Medical  
leakage in pipelines, fittings, joints, service points, and connections [3]. These leakages are usually hard to detect because the WDN pipelines are buried in the ground; therefore, the water may take a long time to appear at the surface. Such leakages result in economic and environmental losses and damage to the surrounding infrastructure [4; 5]; therefore, it is prudent to avoid them. Detecting water leaks at an early stage will improve efficiency in the WDNs. The methods used to detect water leakages can help identify unusual or abnormal pipeline behavior locations. These methods fall into distinct categories: model-based, measurement-based, and emerging practices, such as remote sensing technology. All methods vary in time, accuracy, cost, and ease of implementation. Model-based leakage detection methods rely on software systems, algorithms, statistical analysis tools, and optimization techniques to analyze parameters, including flow rate, pressure, and other measurements [6; 7]. Model-based detection falls under three categories: (a) physical models, (b) statistical models, and (c) machine learning models [8]. The main advantage of physical models is that they do not require a lot of historical data to develop. Secondly, statistical models provide a mathematical model of potential pipe breaks and the likelihood of occurrence. These models are less expensive and time-consuming as they rely on existing data to determine trends [8]. However, model-based leakage detection methods are susceptible to data quality. Inaccurate data will lead to a biased model and erroneous leak detection. Model-based leakage detection methods also face portability challenges due to structural differences within WDNs.