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

Author

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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)

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.

Suggested Citation

  • Ling Kang & Liwei Zhou & Song Zhang, 2017. "Parameter Estimation of Two Improved Nonlinear Muskingum Models Considering the Lateral Flow Using a Hybrid Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(14), pages 4449-4467, November.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:14:d:10.1007_s11269-017-1758-7
    DOI: 10.1007/s11269-017-1758-7
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    References listed on IDEAS

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    1. Majid Niazkar & Seied Hosein Afzali, 2016. "Application of New Hybrid Optimization Technique for Parameter Estimation of New Improved Version of Muskingum Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4713-4730, October.
    2. Xiaohui Yuan & Xiaotao Wu & Hao Tian & Yanbin Yuan & Rana Muhammad Adnan, 2016. "Parameter Identification of Nonlinear Muskingum Model with Backtracking Search Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(8), pages 2767-2783, June.
    3. Ling Kang & Song Zhang, 2016. "Application of the Elitist-Mutated PSO and an Improved GSA to Estimate Parameters of Linear and Nonlinear Muskingum Flood Routing Models," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-20, January.
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    Cited by:

    1. Dariusz Gąsiorowski & Romuald Szymkiewicz, 2020. "Identification of Parameters Influencing the Accuracy of the Solution of the Nonlinear Muskingum Equation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3147-3164, August.
    2. Nazanin Farahani & Hojat Karami & Saeed Farzin & Mohammad Ehteram & Ozgur Kisi & Ahmad Shafie, 2019. "A New Method for Flood Routing Utilizing Four-Parameter Nonlinear Muskingum and Shark Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4879-4893, November.
    3. Wen-chuan Wang & Wei-can Tian & Dong-mei Xu & Kwok-wing Chau & Qiang Ma & Chang-jun Liu, 2023. "Muskingum Models’ Development and their Parameter Estimation: A State-of-the-art Review," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 3129-3150, June.
    4. Chengpeng Lu & Keyan Ji & Wanjie Wang & Yong Zhang & Tema Koketso Ealotswe & Wei Qin & Jiayun Lu & Bo Liu & Longcang Shu, 2021. "Estimation of the Interaction Between Groundwater and Surface Water Based on Flow Routing Using an Improved Nonlinear Muskingum-Cunge Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2649-2666, June.
    5. Amirfarhad Aletaha & Masoud-Reza Hessami-Kermani & Reyhaneh Akbari, 2024. "Enhancing Flood Routing Accuracy: A Fuzzified Approach to Nonlinear Variable-Parameter Muskingum Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(10), pages 3913-3935, August.
    6. Jalal Bazargan & Hadi Norouzi, 2018. "Investigation the Effect of Using Variable Values for the Parameters of the Linear Muskingum Method Using the Particle Swarm Algorithm (PSO)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4763-4777, November.
    7. Reyhaneh Akbari & Masoud-Reza Hessami-Kermani & Saeed Shojaee, 2020. "Flood Routing: Improving Outflow Using a New Non-linear Muskingum Model with Four Variable Parameters Coupled with PSO-GA Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3291-3316, August.

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