ScholarWorks@경상국립대학교

초록

Leakage is one of the representative abnormal conditions in Water distribution systems (WDSs). Leakage can potentially occur and cause immediate economic and hydraulic damage upon occurrence. Therefore, leakage detection is essential, but WDSs are located underground, it is difficult. Moreover, when multiple leakage occurs, it is required to prioritize restoration according to the scale and location of the leakage, applying for an optimal restoration framework can be advantageous in terms of system resilience. In this study, various leakage scenarios were generated based on the WDSs hydraulic model, and leakage detection was carried out containing location and scale using a Deep learning-based model. Finally, the leakage location and scale obtained from the detection results were used as a factor for the priority of leakage restoration, and the results of the priority of leakage restoration were derived. The priority of leakage restoration considered not only hydraulic factors but also socio-economic factors (e.g., leakage scale, important facilities).

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