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Drinking Water Engineering and Science An interactive open-access journal
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https://doi.org/10.5194/dwes-2020-3
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/dwes-2020-3
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 25 Mar 2020

Submitted as: research article | 25 Mar 2020

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A revised version of this preprint is currently under review for the journal DWES.

Prelocalization and Leak detection in water drinking distribution network using modeling-based algorithms: Case study: The city of Casablanca (Morocco)

Faycal Taghlabi1, Laila Sour1, and Ali Agoumi2 Faycal Taghlabi et al.
  • 1Laboratory of Processes and Environment, Faculty of Science and Technology of Mohammedia, Hassan II university of Casablanca, 28806, Morocco
  • 2Laboratory of Civil, hydraulic Engineering, Environment and Climate, Hassania School of Public Works, Casablanca, 20200, Morocco

Abstract. The role of a water drinking distribution network (WDDN) is to supply high-quality water at the necessary pressure at various times of the day for several consumption scenarios. Locating and identifying priorities of water leakage areas becomes major preoccupation for manager of the water supply, to optimize and improve constancy of supply. In this paper, we present the results obtained on the field from a research conducted in order to identify and to locate leaks in (WDDN) focused on the resolution of the Fixed And Variable Area Discharge (FAVAD) equation by use of the prediction algorithms in conjunction with hydraulic modeling and the Geographical Information System (GIS). The leak localization method is applied in the oldest part of Casablanca. We have used, in this research, two methodologies in different leak episodes: (i) The first episode is based on a simulation of artificial leaks on the MATLAB platform using the EPANET code to establish a database of pressures that describe the network's behaviour in the presence of leaks. The data thus established has fed into a machine learning algorithm called Random Forest, which will forecast the leakage rate and its location in the network; (ii) The second was field-testing a real simulation of artificial leaks by opening and closing of hydrants, on different locations with a leak size of 6 l/s and 17 l/s. Results are similar for both methods, the location of leaks is found within 100 meters from the actual leaks.

Faycal Taghlabi et al.

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Faycal Taghlabi et al.

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Latest update: 07 Jul 2020
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Short summary
In this study, we investigate numerically, using EPANET code, the leaks in the water distribution network (WDN). The old part of Casablanca city (Morocco) is used as a case of study. The first episode is based on a simulation of artificial leaks on the MATLAB platform using the EPANET. The second was a real simulation of artificial leaks. The results obtained are compared with those of real simulation of artificial leaks caused by the opening and closing of hydrants.
In this study, we investigate numerically, using EPANET code, the leaks in the water...
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