Solving leakage problem in the water distribution systems (WDSs) typically involves detection and localization of the leak events using a hydraulic model. To achieve the goal, it is often assumed that the hydraulic model is fully calibrated and accurate. On the other hand, from the calibration of the hydraulic model (mainly demand and pipe roughness coefficients; PRCs) perspective, it assumes there is no leakage presenting in the system. In other words, leakage problems and calibration problems set the necessary conditions as the satisfaction of each other’s conditions to solve each problem, respectively. In other words, reliability of the hydraulic model cannot be assured if leakage presents in the WDS. Therefore, there is a need to develop a modeling framework to calibrate the hydraulic model regardless of presence of the leakage. 

This study introduces a real-time leakage detection model by PRC estimation using observed hydraulic information. The central hypothesis is that leakage will result in a fluctuation of estimated PRC as pipe flow changes since pipe with leakage has different behavior in terms of energy dissipation. The PRC estimation model is composed of two different algorithms: (1) PRC estimation using a Markov chain Monte Carlo (MCMC) algorithm; and (2) a leakage detection algorithm using a Kolmogorov–Smirnov (KS) test. The proposed approach is creative and original as this research, to the best of the authors' knowledge, is the first to develop a PRC estimation model capable of estimating both PRC and leakage.

Leakage Detection Based on Pipe Roughness Coefficient Estimation Using Markov Chain Monte Carlo

Created in 1999, the Environmental & Water Resources Institute (EWRI) is the recognized leader within ASCE for the integration of technical expertise and public policy in the planning, design, construction, and operation of environmentally sound and sustainable infrastructure impacting air, land and water resources.

[Paper Proceedings](https://doi.org/10.1061/9780784483466)

Conference DOI: https://doi.org/10.26226/m.6329c39b68fb0e00131f77d2