Bayesian Approach for Joint Estimation of Demand and Roughness in Water Distribution Systems
Publication: Journal of Water Resources Planning and Management
Volume 143, Issue 8
Abstract
A combined demand and roughness estimation is a critical step in order for the water distribution system model to represent the real system adequately. A novel two-level Markov chain Monte Carlo particle filter method for joint estimation of demand and roughness is proposed in this paper. First, an improved particle filter with ensemble Kalman filter modification to proposal density is adopted to track the non-Gaussian system dynamics and estimate demands. Then, the improved particle filter for demand estimation is nested into the Markov chain Monte Carlo simulation for roughness estimation. The method is very capable of quantifying the uncertainties associated with estimated or predicted values without requiring any assumptions of linearity and Gaussianity or any derivatives to be calculated. A strong nonlinear benchmark network with synthetically generated field data is utilized to validate the performance of this method. The results suggest that the proposed method is demonstrated to provide satisfactory demand and roughness values with reliable confidence limits. Some practical issues are also discussed to enhance the application potential of this method.
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Acknowledgments
This work was funded by the National Natural Science Foundation of China (No. U1509208 and 61573313), and the Key Technology Research and Development Program of Zhejiang Province (No. 2015C03G2010034).
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Published In
Journal of Water Resources Planning and Management
Volume 143 • Issue 8 • August 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Sep 28, 2016
Accepted: Feb 26, 2017
Published online: May 19, 2017
Published in print: Aug 1, 2017
Discussion open until: Oct 19, 2017
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