Optimal Sampling Design Methodologies for Water Distribution Model Calibration
Publication: Journal of Hydraulic Engineering
Volume 131, Issue 3
Abstract
Sampling design (SD) for water distribution systems (WDS) is an important issue, previously addressed by various researchers and practitioners. Generally, SD has one of several purposes. The aim of the methodologies developed and presented here is to find the optimal set of network locations for pressure loggers, which will be used to collect data for the calibration of a WDS model. First, existing SD approaches for WDS are reviewed. Then SD is formulated as a multiobjective optimization problem. Two SD models are developed to solve this problem, both using genetic algorithms (GA) as search engines. The first model is based on a single-objective GA (SOGA) approach in which two objectives are combined into one using appropriate weights. The second model uses a multiobjective GA (MOGA) approach based on Pareto ranking. Both SD models are applied to two case studies (literature and real-life problems). The results show several advantages and one disadvantage of the MOGA model when compared to SOGA. A comparison of the MOGA SD model solution to the results of several published SD models shows that the Pareto optimal front obtained using MOGA acts as an envelope to the Pareto fronts obtained using previously published SD models.
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Acknowledgment
Results presented in this paper were obtained within a research project under the United Kingdom Engineering and Physical Sciences Research Council Grant No. GR/M66981/01, which is gratefully acknowledged.
References
Bard, Y. (1974). Nonlinear parameter estimation, Wiley, New York.
Bush, C. A., and Uber, J. G. (1998). “Sampling design methods for water distribution model calibration.” J. Water Resour. Plan. Manage., 124(6), 334–344.
Carrera, J., and Neuman, S. P. (1986). “Estimation of aquifer parameters under transient and steady state conditions: 2. Uniqueness, stability and solution algorithms.” Water Resour. Res., 22(2), 211–227.
de Schaetzen, W. (2000). “Optimal calibration and sampling design for hydraulic network models.” PhD thesis, Univ. of Exeter, Exeter, U.K.
Ferreri, G. B., Napoli, E., and Tumbiolo, A. (1994). “Calibration of roughness in water distribution networks.” Proc., 2nd Int. Conf. on Water Pipeline Systems, BHR Group, Edinburgh, U.K., 379–396.
Fonseca, C. M., and Fleming, P. J. (1993). “Genetic algorithms for multi-objective optimisation: Formulation, discussion and generalisation.” Proc., 5th Int. Conf. on Genetic Algorithms, S. Forrest, ed., Morgan Kaufman, Mateo, Calif., 416–423.
Goldberg, D. E. (1989). Genetic algorithms in search, optimisation and machine learning, Addison-Wesley, Reading, Mass.
Kapelan, Z. (2002). “Calibration of WDS hydraulic models.” PhD thesis, Univ. of Exeter, Exeter, U.K.
Lansey, K. E., and Basnet, C. (1991). “Parameter estimation for water distribution networks.” J. Water Resour. Plan. Manage., 117(1), 126–144.
Lansey, K. E., El-Shorbagy, W., Ahmed, I., Araujo, J., and Haan, C. T. (2001). “Calibration assessment and data collection for water distribution networks.” J. Hydraul. Eng., 127(4), 270–279.
Meier, R. W., and Barkdoll, B. D. (2000). “Sampling design for network model calibration using genetic algorithms.” J. Water Resour. Plan. Manage., 126(4), 245–250.
Morley, M. S., Atkinson, R. M., Savic, D. A., and Walters, G. A. (2001). “GAnet: Genetic algorithm platform for pipe network optimisation.” Adv. Eng. Software, 32(6), 467–475.
Ormsbee, L. E. (1989). “Implicit network calibration.” J. Water Resour. Plan. Manage., 115(2), 243–257.
Piller, O., Bremond, B., and Morel, P. (1999). “A spatial sampling procedure for physical diagnosis in a drinking water supply network.” Proc., Water Industry Systems: Modelling and Optimisation Applications, D. A. Savic and G. A. Walters, eds., Vol. 1, Research Studies Press Ltd., Exeter, U.K., 309–316.
Press, W. H., Flannery, B. P., Teukolsky, S. A., and Vetterling, W. T. (1990). Numerical recipes: The art of scientific computing, Cambridge Univ. Press, Cambridge, U.K.
Rossman, L. A. (2000). Epanet2 users manual, US EPA, Washington, D.C.
Shamir, U., and Howard, C. D. D. (1968). “Water distribution systems analysis.” J. Hydraul. Div., Am. Soc. Civ. Eng., 94(1), 219–234.
Veldhuizen, D. A. V., and Lamont, G. B. (1998). “Multiobjective evolutionary algorithm research: A history and analysis.” TR-98-03, Department of Electrical and Computer Engineering, Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio.
Walski, T. M. (1983). “Technique for calibrating network models.” J. Water Resour. Plan. Manage., 109(4), 360–372.
Walski, T. M., et al. (1987). “Battle of network models: Epilogue.” J. Water Resour. Plan. Manage., 113(2), 191–203.
Yu, G., and Powell, R. S. (1994). “Optimal design of meter placement in water distribution systems.” Int. J. Syst. Sci., 25(12), 2155–2166.
de Schaetzen, W., Walters, G. A., and Savic, D. A. (2000). “Optimal sampling design for model calibration using shortest path, genetic and entropy algorithms.” Urban Water, 2, 141–152.
Information & Authors
Information
Published In
Journal of Hydraulic Engineering
Volume 131 • Issue 3 • March 2005
Pages: 190 - 200
Copyright
© 2005 ASCE.
History
Received: Jun 4, 2003
Accepted: Mar 26, 2004
Published online: Mar 1, 2005
Published in print: Mar 2005
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