This study proposes a method for the identification of the spatial distribution of water losses in water distribution networks through the use of pressure head measurements. The proper identification of areas most prone to water losses reduces the costs associated with acoustic surveys both in terms of number of pipes to be examined and working time. To get the best estimate of the water losses spatial distribution, data assimilation techniques based on the Kalman Filter approach (Ensemble Kalman Filter and Ensemble Smoother) are coupled with the hydraulic network model (EPANET). The coupled model performances are investigated on the Anytown benchmark system with both a known and an unknown consumption pattern. A method to identify the most effective network monitoring locations is also proposed. Despite the fact that the method is tested on a single synthetic network, the results suggest that the tool is promising for water losses identification.

Losses identification in water distribution networks through EnKF and ES

RUZZA, VALENTINA;CRESTANI, ELENA;SALANDIN, PAOLO
2015

Abstract

This study proposes a method for the identification of the spatial distribution of water losses in water distribution networks through the use of pressure head measurements. The proper identification of areas most prone to water losses reduces the costs associated with acoustic surveys both in terms of number of pipes to be examined and working time. To get the best estimate of the water losses spatial distribution, data assimilation techniques based on the Kalman Filter approach (Ensemble Kalman Filter and Ensemble Smoother) are coupled with the hydraulic network model (EPANET). The coupled model performances are investigated on the Anytown benchmark system with both a known and an unknown consumption pattern. A method to identify the most effective network monitoring locations is also proposed. Despite the fact that the method is tested on a single synthetic network, the results suggest that the tool is promising for water losses identification.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3143333
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