Leak localization in water distribution networks using Bayesian classifiers

Journal Article (2017)


Journal of Process Control





Doc link


Download the digital copy of the doc pdf document


This paper presents a method for leak localization in water distribution networks (WDNs) based on Bayesian classifiers. Probability density functions for pressure residuals are calibrated off-line for all the possible leak scenarios by using a hydraulic simulator, and considering the leak size uncertainty, demand uncertainty and sensor noise. A Bayesian classifier is applied on-line to the computed residuals to determine the location of leaks in the WDN. A time horizon based reasoning combined with the Bayesian classifier is also proposed to improve the localization accuracy. Two case studies based on the Hanoi and the Nova Icària networks are used to illustrate the performance of the proposed approach. Simulation results are presented for the Hanoi case study, whereas results for a real leak scenario are shown for the Nova Icària case study.



Author keywords

Fault diagnosis; Bayesian classifier; Water distribution networks; Leak localization

Scientific reference

A. Soldevila, R.M. Fernandez-Cantí, J. Blesa, S. Tornil-Sin and V. Puig. Leak localization in water distribution networks using Bayesian classifiers. Journal of Process Control, 55: 1-9, 2017.