Estimation of Nonlinear Muskingum Model Parameter Using Differential Evolution
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Volume 17, Issue 2
Abstract
The accurate estimation of Muskingum model parameter is essential to give flood routing for flood control in water resources management. The Muskingum model continues to be a popular method for flood routing, and its parameter estimation is a global optimization problem with the main objective to find a set of optimal model parameter values that attains a best fit between observed and computed flow. Although some techniques have been employed to estimate the parameters for Muskingum model, an efficient method for parameter estimation in Muskingum model is still required to improve the computational precision. Therefore, in this paper, the differential evolution (DE) algorithm is studied for estimation of Muskingum model parameter. A case study with actual data from previous literature, the experimental results showed an excellent performance in its optimization result and performance analysis and demonstrates that DE is an alternative technique to estimate the parameters of the Muskingum model.
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Acknowledgments
This work is supported by the Natural Science Research Program of Henan Province Educational Committee (No. UNSPECIFIED2010B570002) and the Scientific Research Foundation of North China Institute of Water Conservancy and Hydroelectric Power for High-Level Talents (No. UNSPECIFIED200821). The writers gratefully acknowledge the many suggestions and comments provided by the editors and anonymous reviewers, which have greatly improved the paper.
References
Bretscher, O. (1995). Linear algebra with applications, 3rd Ed., Prentice Hall, Upper Saddle River, NJ.
Chu, H. J., and Chang, L. C. (2009). “Applying particle swarm optimization to parameter estimation of the nonlinear Muskingum model.” J. Hydrol. Eng., 14(9), 1024–1027.
Das, A. (2004). “Parameter estimation for Muskingum models.” J. Irrig. Drain Eng., 130(2), 140–147.
Das, A. (2007). “Chance-constrained optimization-based parameter estimation for Muskingum models.” J. Irrig. Drain Eng., 133(5), 487–494.
Galvin, G., and Houck, M. H. (1985). “Optimal Muskingum River routing.” Computer applications in water resources, ASCE, New York, 1294–1302.
Geem, Z. W. (2006). “Parameter estimation for the nonlinear muskingum model using the BFGS technique.” J. Irrig. Drain Eng., 132(5), 474–478.
Geem, Z. W., Kim, J. H., and Loganathan, G. V. (2001). “A new heuristic optimization algorithm: Harmony search.” Simulation, 76(2), 60–68.
Gill, M. A. (1978). “Flood routing by the Muskingum method.” J. Hydrol. (Amsterdam), 36(3–4), 353–363.
Hooke, R., and Jeeves, T. A. (1961). “Direct search solution of numerical and statistical problems.” J. Assoc. Comput. Mach., 8(2), 212–229.
Kennedy, J., and Eberhart, R. (1995). “Particle swarm optimization.” Proc., IEEE Int. Conf. on Neural Networks, Perth, Australia, Vol. 4, 1942–1948.
Kim, J. H., Geem, Z. W., and Kim, E. S. (2001). “Parameter estimation of the nonlinear Muskingum model using Harmony search.” J. Am. Water Resour. Assoc., 37(5), 1131–1138.
McCarthy, G. T. (1938). “The unit hydrograph and flood routing.” Proc., Conf. of North Atlantic Division, U.S. Army Corps of Engineers, Rhode Island.
Michalewicz, Z. (1996). Genetic algorithm + data structure = evolution programs, 3rd Ed., Springer-Verlag, New York.
Mohan, S. (1997). “Parameter estimation of nonlinear Muskingum models using genetic algorithm.” J. Hydraul. Eng., 123(2), 137–142.
Nazareth, L. (1979). “A relationship between the BFGS and conjugate gradient algorithms and its implications for new algorithms.” SIAM J. Numer. Anal., 16(5), 794–800.
Price, K. V., Storn, R. M., and Lampinen, J. A. (2005). Differential evolution: A practical approach to global optimization, Springer-Verlag, Berlin.
Samani, H. M. V., and Jebelifard, S. (2003). “Design of circular urban storm sewer systems using multilinear Muskingum flow routing method.” J. Hydraul. Eng., 129(11), 832–838.
Storn, R., and Price, K. (1997). “Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces.” J. Global Optim., 11(4), 341–359.
Tung, Y. K. (1985). “River flood routing by nonlinear Muskingum method.” J. Hydraul. Eng., 111(12), 1447.
Wilson, E. M. (1974). Engineering hydrology, MacMillan Education, Hampshire, UK.
Wilson, E. M. (1990). Engineering hydrology, Macmillan Education, Hampshire, UK.
Yoon, J. W., and Padmanabhan, G. (1993). “Parameter-estimation of linear and nonlinear muskingum models.” J. Water Resour. Plann. Manage., 119(5), 600–610.
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Published In
Journal of Hydrologic Engineering
Volume 17 • Issue 2 • February 2012
Pages: 348 - 353
Copyright
© 2012 American Society of Civil Engineers.
History
Received: Jun 3, 2010
Accepted: May 19, 2011
Published online: May 21, 2011
Published in print: Feb 1, 2012
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