# GIS-Based Identification of Locations in Water Distribution Networks Vulnerable to Leakage
## 5 Conclusions  
This paper presents a new leakage detection method. The method uses logistic regression and vulnerability analysis implemented in a GIS environment. The logistic regression model considered six parameters as conditioning factors for leakage, including three fixed and three dynamic variables. The six parameters include pipe length per junction, number of fittings per length, and pipe friction factor, as well as three variable operational factors including drop in pressure, decrease in flow, and variations in chlorine levels (as indicators of change in water quality). To validate the model, 13 DMAs of the SEWA WDN were used. The method successfully created vulnerability maps considering the maximum, minimum, and average drops in the varying factors (flow, pressure, chlorine). Knowing that flow, pressure, and chlorine values are measured by many utility companies on an hourly basis throughout the WDN daily, the vulnerability maps will be updated accordingly. These \"dynamic\" maps can help identify network parts that are vulnerable to leakage. A main advantage of this method is its simplicity and ease of implementation, especially for corrective maintenance activities. A major limitation to the method developed in this research is the need to access the water flow, pressure, and chlorine throughout the WDN, which are measured on an hourly basis for many WDNs. Moreover, although access to such data can be challenging, in future studies it is recommended to include more parameters that influence leakages, such as the age of the WDN pipes and the type of soil where the pipes are buried. Future work can investigate the contribution of each of the parameters considered in this study to water leakage in the WDN, which will help to robustly determine the weights assigned for each parameter. Finally, water utility companies are encouraged to share their WDN data, which will help to support research activities in this area. Future research efforts should focus on identifying the cause of potential failure. Subsequent categorization and classification of these sources of pipe failure could help enhance the detection model. It is crucial to note that pipe leakage stems from diverse causes. Accounting for failure models will also improve model sensitivity while enhancing applicability in different sectors. The detection model could include data on WDN isolation locations for valves. Incorporating these locations in the model will improve detection measures. For automated systems, mapped valve locations can help management teams respond swiftly by isolating leaking pipes in the WDN. **Author Contributions:** Conceptualization, T.A. and S.A.; methodology, T.A. and E.A. ; validation, A.G.Y. ; formal analysis, E.A., S.A. and T.A. ; investigation, resources, M.M.M. and K.P.F. ; data curation, S.A. and T.A., writing--original draft preparation, E.A. ; writing--review and editing, K.P.F., Z.K., T.A., S.A. and M.M.M. ; visualization, S.A. and T.A. ; supervision, S.A. and T.A. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article. **Acknowledgments:** The work in this paper was supported, in part, by the Open Access Program from the American University of Sharjah. This paper represents the opinions of the authors and does not mean to represent the position or opinions of the American University of Sharjah. The authors would like to acknowledge partial support of this work provided by the American University of Sharjah with grant number FRG22-C-E15. Appreciative acknowledgement also goes to Sharjah Electricity and Water Authority (SEWA) for providing the data and information required for completing this study. **Conflicts of Interest:** The authors declare no conflict of interest.