Pressure-Dependent Leak Detection Model and Its Application to a District Water System
Publication: Journal of Water Resources Planning and Management
Volume 136, Issue 1
Abstract
Cost-effective reduction of water loss is a compelling but challenging task for water utilities. This paper presents a model-based optimization method for leakage detection of water distribution systems. Leakage hotspots are assumed to exist at the model nodes identified. Leakage is represented as pressure-dependent demand simulated as emitter flows at selected model nodes. The leakage detection method is formulated to optimize the leakage node locations and their associated emitter coefficients such that the differences between the model predicted and the field observed values for pressure and flow are minimized. The optimization problem is solved by using a competent genetic algorithm. The leakage detection method is developed as an add-on feature of the optimization-based model calibration tool. This enables engineers to undertake leakage hotspot optimization as an independent task or combine the task with hydraulic model calibration, subject to suitably varied field data. Two case studies are discussed in this paper including an example from literature and a district water system in the United Kingdom. The results obtained illustrate that the optimization model for predicting leakage hotspots can be effective despite the recognized challenges of model calibration and the physical measurement limitations from the pressure and flow surveys also referred to as field tests. It is found that the method is effective at being applied for hydraulic conditions that occur in the early hours of the morning, often on water networks with excess design capacity and where hydraulic gradients are slack and loggers may sometimes be working close to their limits of accuracy.
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 136 • Issue 1 • January 2010
Pages: 116 - 128
Copyright
© 2010 ASCE.
History
Received: Apr 30, 2008
Accepted: May 11, 2009
Published online: Dec 15, 2009
Published in print: Jan 2010
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