Parameter Estimation of Two Improved Nonlinear Muskingum Models Considering the Lateral Flow Using a Hybrid Algorithm

Ling Kang, Liwei Zhou () and Song Zhang
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Ling Kang: Huazhong University of Science and Technology
Liwei Zhou: Huazhong University of Science and Technology
Song Zhang: China Three Gorges Corporation, The Three Gorges Dam District

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2017, vol. 31, issue 14, No 5, 4449-4467

Abstract: Abstract The Muskingum model was one of the most popular methods for flood routing in water resources engineering, many researchers had presented various versions of Muskingum model so as to enhance the precision of the Muskingum model in their papers. Similarly, two new nonlinear Muskingum models were presented in this paper. One considered the lateral flow, and the other considered the lateral flow and a variable exponent parameter, simultaneously. Minimizing the sum of the squared (SSQ) deviations between the observed and routed outflows was considered as the objective, and then three benchmark examples and a real example in Iran were applied to verify performances of two proposed models. A hybrid algorithm, which combined the improved real-coded adaptive genetic algorithm and the Nelder-Mead simplex algorithm, was utilized for parameter estimation of two proposed models. Comparisons of the optimal results for four examples by different models showed that two proposed models can produce more accurate fit to observed outflows, and the proposed model, which simultaneously considered a variable exponent parameter and the lateral flow, reduced the SSQ obviously.

Keywords: Flood routing; Hydrologic method; Nonlinear Muskingum model; Variable exponent; Lateral flow (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s11269-017-1758-7

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