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Parameter Identification of Nonlinear Muskingum Model with Backtracking Search Algorithm

Author

Listed:
  • 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)

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.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:8:d:10.1007_s11269-016-1321-y
    DOI: 10.1007/s11269-016-1321-y
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    References listed on IDEAS

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    2. Zaw Latt, 2015. "Application of Feedforward Artificial Neural Network in Muskingum Flood Routing: a Black-Box Forecasting Approach for a Natural River System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 4995-5014, November.
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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. 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.
    3. Chen, Zhihuan & Yuan, Xiaohui & Yuan, Yanbin & Lei, Xiaohui & Zhang, Binqiao, 2019. "Parameter estimation of fuzzy sliding mode controller for hydraulic turbine regulating system based on HICA algorithm," Renewable Energy, Elsevier, vol. 133(C), pages 551-565.
    4. 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.
    5. 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.
    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.
    8. Hassan, Bryar A. & Rashid, Tarik A., 2020. "Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation," Applied Mathematics and Computation, Elsevier, vol. 370(C).
    9. Aryan Salvati & Alireza Moghaddam Nia & Ali Salajegheh & Parham Moradi & Yazdan Batmani & Shahabeddin Najafi & Ataollah Shirzadi & Himan Shahabi & Akbar Sheikh-Akbari & Changhyun Jun & John J. Clague, 2023. "Performance improvement of the linear muskingum flood routing model using optimization algorithms and data assimilation approaches," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(3), pages 2657-2690, September.

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