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Hybrid particle swarm optimization for parameter estimation of Muskingum model

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Abstract

The Muskingum model is the most widely used and efficient method for flood routing in hydrologic engineering; however, the applications of this model still suffer from a lack of an efficient method for parameter estimation. Thus, in this paper, we present a hybrid particle swarm optimization (HPSO) to estimate the Muskingum model parameters by employing PSO hybridized with Nelder–Mead simplex method. The HPSO algorithm does not require initial values for each parameter, which helps to avoid the subjective estimation usually found in traditional estimation methods and to decrease the computation for global optimum search of the parameter values. We have carried out a set of simulation experiments to test the proposed model when applied to a Muskingum model, and we compared the results with eight superior methods. The results show that our scheme can improve the search accuracy and the convergence speed of Muskingum model for flood routing; that is, it has higher precision and faster convergence compared with other techniques.

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Acknowledgments

This paper is partially funded by the Key Program of National Natural Science Foundation of China (Grant No. 61133005), and the National Natural Science Foundation of China (Grant Nos. 90715029, 61070057, 60603053, 61103047), and the Ph.D. Programs Foundation of Ministry of Education of China (20100161110019). Meanwhile, the paper was supported by the Research Foundation of Education Bureau of Hunan Province (No.13C333), the Project and the Science and Technology Research Foundation of Hunan Province (Grant No. 2014GK3043).

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China

    Aijia Ouyang & Kenli Li

  2. Faculty of Information Technology, Industrial University of Hochiminh City, Hochiminh, Vietnam

    Tung Khac Truong

  3. Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt

    Ahmed Sallam

  4. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Edwin H.-M. Sha

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  1. Aijia Ouyang
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  2. Kenli Li
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  3. Tung Khac Truong
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  4. Ahmed Sallam
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  5. Edwin H.-M. Sha
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Correspondence to Kenli Li.

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Ouyang, A., Li, K., Truong, T.K. et al. Hybrid particle swarm optimization for parameter estimation of Muskingum model. Neural Comput & Applic 25, 1785–1799 (2014). https://doi.org/10.1007/s00521-014-1669-y

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  • DOI: https://doi.org/10.1007/s00521-014-1669-y

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