Parameter Identification of Nonlinear Muskingum Model with Backtracking Search Algorithm

Xiaohui Yuan, Xiaotao Wu, Hao Tian, Yanbin Yuan () and Rana Muhammad Adnan
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Xiaohui Yuan: Huazhong University of Science and Technology
Xiaotao Wu: Huazhong University of Science and Technology
Hao Tian: Huazhong University of Science and Technology
Yanbin Yuan: Wuhan University of Technology
Rana Muhammad Adnan: Huazhong University of Science and Technology

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2016, vol. 30, issue 8, No 12, 2767-2783

Abstract: Abstract Nonlinear Muskingum model is a popular approach widely used for flood routing in hydraulic engineering. An improved backtracking search algorithm (BSA) is proposed to estimate the parameters of nonlinear Muskingum model. The orthogonal designed initialization population strategy and chaotic sequences are introduced to improve the exploration and exploitation ability of BSA. At the same time, a selection strategy based individual feasibility violation is developed to ensure that the computed outflows are non-negative in the evolutionary process. Finally, three examples are employed to demonstrate the performance of the improved BSA. The comparison between the results of routing outflows and those of Wilcoxon signed ranks test shows that the improved BSA outperforms particle swarm optimization, genetic algorithm, differential evolution and other algorithms reported in the literature in terms of solution quality. Therefore, it is reasonable to draw the conclusion that the proposed BSA is a satisfactory and efficient choice for parameter estimation of nonlinear Muskingum model.

Keywords: Nonlinear Muskingum model; Parameter identification; Backtracking search algorithm; Flood routing; Wilcoxon signed ranks test (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11269-016-1321-y

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