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Application of New Hybrid Optimization Technique for Parameter Estimation of New Improved Version of Muskingum Model

Author

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  • Majid Niazkar

    (Shiraz University)

  • Seied Hosein Afzali

    (Shiraz University)

Abstract

The Muskingum method is one of the most utilized lumped flood routing model in which calibration of its parameters provides an active area of research in water resources engineering. Although various techniques and versions of Muskingum model have been presented to estimate the parameters of different versions of Muskingum model, more rigorous approaches and models are still required to improve the computational precision of calibration process. In this study, a new hybrid technique was proposed for Muskingum parameter estimation which combines the Modified Honey Bee Mating Optimization (MHBMO) and Generalized Reduced Gradient (GRG) algorithms. According to the conducted literature-review on the improvement of Muskingum flood routing models, a new six-parameter Muskingum model was proposed. The hybrid technique was successfully applied for parameter estimation of this new version of Muskingum model for three case studies selected from literature. The obtained results were compared with those of other methods using several common performance evaluation criteria. The new hybrid method with the new proposed Muskingum model perform the best among all the considered approaches based on most of utilized criteria. The new Muskingum model significantly reduces the SSQ value for the double-peak case study. Finally, the achieved results demonstrate that not only the hybrid MHBMO-GRG algorithm overcomes the shortcomings of both phenomenon-mimicking and mathematical optimization techniques, but also the presented Muskingum model is appeared to be the most reliable version of Muskingum model comparing with other considered models in this research.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:13:d:10.1007_s11269-016-1449-9
    DOI: 10.1007/s11269-016-1449-9
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    References listed on IDEAS

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    1. 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.
    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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    Citations

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    Cited by:

    1. Majid Niazkar & Nasser Talebbeydokhti & Seied Hosein Afzali, 2019. "Novel Grain and Form Roughness Estimator Scheme Incorporating Artificial Intelligence Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 757-773, January.
    2. Majid Niazkar & Nasser Talebbeydokhti & Seied Hosein Afzali, 2019. "One Dimensional Hydraulic Flow Routing Incorporating a Variable Grain Roughness Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4599-4620, October.
    3. Wanlong Yang & Jun Wang & Jueyi Sui & Fangxiu Zhang & Baosen Zhang, 2019. "A Modified Muskingum Flow Routing Model for Flood Wave Propagation during River Ice Thawing-Breakup Period," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4865-4878, November.
    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. Mike Spiliotis & Alvaro Sordo-Ward & Luis Garrote, 2021. "Estimation of Fuzzy Parameters in the Linear Muskingum Model with the Aid of Particle Swarm Optimization," Sustainability, MDPI, vol. 13(13), pages 1-26, June.
    7. 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.
    8. 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.
    9. 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.
    10. Majid Niazkar, 2020. "Discussion of “Accurate and Efficient Explicit Approximations of the Colebrook Flow Friction Equation Based on the Wright ω-Function” by Dejan Brkić and Pavel Praks, Mathematics 2019, 7 , 34; doi:10.3," Mathematics, MDPI, vol. 8(5), pages 1-6, May.
    11. 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.
    12. Esmatullah Sangin & S. K. Mishra & Pravin R. Patil, 2024. "Analogy Between SCS-CN and Muskingum Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 153-171, January.

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