Roughness and Demand Estimation in Water Distribution Networks Using Head Loss Adjustment
Publication: Journal of Water Resources Planning and Management
Volume 143, Issue 12
Abstract
To estimate pipe roughness and nodal demand parameters in water distribution networks, a method based on head loss adjustment is proposed. By using weighted least squares (WLS), model-simulated head losses are adjusted to minimize the sum of the squares of the corrections (differences between simulated and adjusted values) of simulated head losses under the constraints of head and flow measurements. Pipe roughness coefficients (Hazen-Williams -factors) are computed by using the ratio of the simulated to adjusted head losses and refined by using boundary-fence and gross-error detection techniques. With the adjusted -factors and head losses, pipe flows are computed to rectify nodal demands, which are limited in their boundary fences. After the demand multipliers are computed and filtered by using gross-error detection techniques, a large number of nodal demands make an advance. The loop of model simulation, head loss adjustment, and roughness and demand calibrations runs iteratively from different starting points of -factor under multiple loading conditions, then realistic roughness and demands are achieved. The method was verified on two benchmark networks—a hypothetical network and a real network—by using noisy head and flow measurements. The results show that the method is effective for dual estimation of roughness and demand parameters.
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Acknowledgments
The study was developed within the framework of the Second Liaoning Medium Cities Infrastructure Project, GEF Grant Number TF057757_CHA, and partially supported by Natural Science Foundation of China under Grant No. 41671404. The author thanks the reviewers for their thorough and insightful review that improved the quality of the paper.
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Published In
Journal of Water Resources Planning and Management
Volume 143 • Issue 12 • December 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Jan 14, 2017
Accepted: Jun 1, 2017
Published online: Sep 28, 2017
Published in print: Dec 1, 2017
Discussion open until: Feb 28, 2018
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