Report Open Access

Leak Localization In Water Distribution Networks Using Data-Driven And Model-Based Approaches

ROMERO, Luis; BLESA, Joaquim; ALVES, Débora; CEMBRANO, Gabriela; PUIG, Vicenç; DUVIELLA, Eric

The worldwide growing demand of water supply requires a proper management of the available hydraulic resources. One of the major concerns in the operation of water distribution networks (WDNs) is the existence of leakages, due to the high operational costs for the water utilities. Leaks can produce substantial economic losses, infrastructure damage and even health risks. Therefore, leak detection and isolation methodologies are widely researched.
One the one hand, model-based approaches exploit the existence of a hydraulic model of the considered WDN, as well as the availability of hydraulic measurements like inlet flow and pressure, and sensorized inner nodes pressure, to tackle the leak localization task. The suitability of these methods has been confirmed by numerous works during the years. On the other hand, the sources of information in the majority of water networks are rather limited, and other interesting measurements are not available, like water demands at the junctions, flows between inner nodes, etc. Thus, data-driven approaches, which have a reduced or non-existent dependency on a hydraulic model, can be helpful to locate leaks in WDNs that lack the mentioned measurements and modelling.
This abstract presents the combined utilization of a model-based and a novel data-driven methodology to locate leaks in the concrete case of the challenge proposed at BattLeDIM 2020. The division of the introduced network (L-Town) in three areas allows to determine the usage of one of the approaches at each one of these areas, depending on their concrete characteristics.
Besides, both methods allow to solve the multi-leak problem in a proper way, which entails a further step with regard to the classical single-leak assumption.

Files (604.3 kB)
Name Size
604.3 kB Download
All versions This version
Views 240240
Downloads 228228
Data volume 137.8 MB137.8 MB
Unique views 204204
Unique downloads 210210


Cite as