Non-Revenue Water: Methodological Comparative Assessment

  • Maraheb Saqer Al-Ali

    Student thesis: Master's Thesis


    Population growth and development are continuously increasing the pressure on water demand with chronic shortages becoming the norm in many areas. As such, it is imperative to minimize losses in water networks which can be achieved by assessing and quantifying the volume of losses then implementing prevention and early detection techniques that hinder small leaks from progressing to larger ones. This study presents a comparative assessment of various methods of estimating losses in water distribution networks noting their corresponding advantages and disadvantages aimed towards improved management of water networks. It examined the Top-Down Annual Water Balance, Water and Wastewater Balance, Minimum Night Flow Analysis, Component Analysis of Leakage, and machine learning applications to report real time leaks. Selected methods were assessed in a real-life application of an Abu Dhabi based water transmission network to test their effectiveness in capturing non-revenue water losses. Results indicated that the Top-Down Annual Water Balance and the Component Analysis of Leakage are the most efficient and least demanding approaches in extracting meaningful information from routine data to gain insight pertaining to performance and operational efficiency with least amount of uncertainty. When methods were applied, they complemented one another and helped develop a monitoring and management policy for prioritizing interventions towards minimizing non-revenue water. The water control strategy helped recognize and recover more than half of the volume of real losses and reduce real losses to the lowest economically feasible level thereby reducing non-revenue water to target levels and elevating performance when measured against best practice key performance indicators. However, this work was limited as it applied methods proposed for the use of distribution networks to a transmission network. Future work will be directed towards repeating the application on a larger scale to further corroborate the results, check the validity of assumptions made, and track progress.
    Date of AwardMay 2022
    Original languageAmerican English


    • Non-Revenue Water; Water loss assessment; Water balance; Component analysis of leakage; Water leakage control.

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