Leakage Identification in Water Distribution Networks Based on Physics-Based Joint Inversion and ExtraTrees Candidate Re-Ranking

Leakage identification in water distribution networks must estimate leak locations and magnitudes under demand fluctuations, similar adjacent-node responses, and superimposed multiple-leak signals. This study combines physics-based joint inversion with ExtraTrees candidate re-ranking. Using an EPANET model of the Hanoi network, emitter candidates were placed at pipe midpoints, and scenarios were generated across demand periods, global demand factors, and four regional demand fluctuations. Sixteen pressure residuals and 22 flow residuals formed the observation vector. The physical stage enumerated zero- to three-leak combinations and used bounded least squares to estimate leakage flows and demand corrections; standardized pressure-flow residuals and penalty terms produced the candidate pool. ExtraTrees then re-ranked candidates using candidate structure, physical scores, flow statistics, and operating-period features. In 2000 independent blind-test scenarios, complete localization accuracy, leak-number identification accuracy, and total leakage-flow MAE were 90.55%, 97.10%, and 0.841 L/s; for 1500 leakage scenarios, they were 87.53%, 96.27%, and 1.117 L/s. Re-ranking increased leakage-scenario complete localization from 76.13% to 87.53%, while final candidate-pool recall reached 99.15%. Robustness tests involving measurement noise, hydraulic-model mismatch, sensor density, computational time, and Net1 indicated that the method improves candidate discrimination under simulation, although roughness bias and weak multiple-leak signals remain challenging.