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Parameters Estimation of the Nonlinear Muskingum Flood-Routing Model Using Wolf Search Algorithm (WSA) (Case Study: Kardeh River) | ||
| تحقیقات مهندسی سازه های آبیاری و زهکشی | ||
| Article 8, Volume 17, Issue 67, March 2017, Pages 95-112 PDF (1.27 M) | ||
| Document Type: Original Article | ||
| DOI: 10.22092/aridse.2017.109605 | ||
| Author | ||
| Saeid Akbarifard* | ||
| Abstract | ||
| Flood routing is one of the most complex problems that is investigated in open channel hydraulics and river engineering. Among the different flood routing methods, the Muskingum model, as the most common hydrologic method of flood routing, has been widely used with high accuracy in river flood studies. The parameters estimation of the nonlinear Muskingum flood-routing model has been considered by different researchers and several methods have been utilized to this purpose. In this paper, the wolf search algorithm (WSA) was used to this end. To assess the optimum values of Muskingum parameters, the objective function was defined as the minimizing of the sum of square residuals between the observed and routed outflows. To investigate the desirability of research findings, the results of the WSA were compared with other heuristic algorithms including genetic algorithm (GA), particle swarm optimization (PSO), harmony search (HS), and imperialist competitive algorithm (ICA). Six benchmark functions with different dimensions were used to evaluate the capability of algorithms. The results showed that the WSA is capable to provide satisfactory estimates of nonlinear Muskingum parameters, so that, the values of R2 and RMSE were obtained 0.99261 and 2.419886 for Kardeh river and 0.778425 and 0.712358 for Wilson river, respectively. | ||
| Keywords | ||
| Flood routing; Hydrologic model; optimization; Wolf Search Algorithm | ||
| References | ||
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Atashpaz-Gargari, C. L. and Lucas, C. 2007. Imperialist competitive algorithm: an algorithm for optimization inspires by imperialistic competition. IEEE Congress on Evolutionary Computation. Sep. 25-27. Singapore.
Barati, R., Akbari, Gh. and Arami, M. 2010. New algorithm for estimating the non-linear Maskingham relatioen. 9th Conference of Hydraulic. Nov. 8-10. Tehran. Iran. (in Persian)
Chaudhury, P., Shrivastava, R. and Narulkar, S. 2002. Flood routing in river networks using equivalent Muskingum inflow. J. Hydrol. Eng. 7, 413-419.
Chen, J. and Yang, X. 2007. Optimal parameter estimation for Muskingum model based on gray encoded accelerating genetic algorithm. Commun. Nonlinear Sci. Numer. Simul. 12(5): 849-858.
Chow, V. T. 1973. Open Channel Hydraulic. 3rd Ed. McGraw Hill Book Company. New York. Inc.
Chu, H. J. and Chang, L. C. 2009. Applying particle swarm optimization to parameter estimation of the nonlinear Muskingum model. J. Hydrol. Eng. 14, 1024-1027.
Das, A. 2004. Parameter estimation for Muskingum models. J. Irrig. Drain. Eng. 2, 140-147.
Geem, Z. W. 2000. Optimal design of water distribution networks using harmony search. Ph. D Thesis. Department of Civil and Environmental Engineering. Korea University.
Gill, M. A. 1978. Flood routing by Muskingum method. J. Hydrol. 36, 353-363.
Hamedi, F., Bozorg Haddad, O. and Vatankhah, A. 2012. Improving the non-linear Maskingham model using a novel hybrid storage model. 5th Conference of Water Resources Management. Feb. 17-18. Tehran. Iran. (in Persian)
Holland, J. 1975. Adaptation in Natural and Artificial System. University of Michigan Press.
Karahan, H., Gurarslan, G. and Geem, Z. W. 2013. Parameter estimation of the nonlinear Muskingum flood-routing model using a hybrid harmony search algorithm. J. Hydrol. Eng. 18, 352-360.
Kennedy, J. and Eberhart, R. 1995. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Network. Perth. Australia.
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. As. 37, 1131-1138.
McCarthy, G. T. 1938. The unit hydrograph and flood routing. Proceeding of Conference of North Atlantic Division. U. S. Army Corps of Engineers. Washington, DC.
Mohammadi-Ghaleni, M., Bozorg-Haddad, O. and Ebrahimi, K. 2010. Optimizing the non-linear Muskingum parameters by SA algorithm. J. Soil Water. 24(5): 908-919. (in Persian)
Mohan, S. 1997. Parameter estimation of nonlinear Muskingum models using genetic algorithm.
Samani, H. M. V. and Shamsipour, G. A. 2004. Hydrologic flood routing in branched river systems via nonlinear optimization. J. Hydraul. Res. 42(1): 55-59.
Shah-Hosseini, Sh., Moosavi, H. M. and Mollajafari, M. 2011. Meta-Heuristic Algorithms: Theory and Implementation in Matlab. Iran University of Science and Technology Pub. (in Persian)
Singh, V. P. and Scarlatos, P. D. 1987. Analysis of nonlinear Muskingum flood routing. J. Hydrol. Eng. 113, 61-79.
Tang, R., Fong, S., Yang, X. S. and Deb, S. 2012. Integrating nature-inspired optimization algorithm to
Tung, Y. K. 1985. River flood routing by nonlinear Muskingum method. J. Hydrol. Eng. 111,
Wilson, E. M. 1974. Engineering Hydrology. 2nd Ed. MacMillan Pub. United Kingdom. | ||
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