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Calibration of Linear Muskingum Model Coefficients and Coefficient Parameters Using Grey Wolf and Particle Swarm Optimization

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  • Kemal Saplıoğlu

    (Süleyman Demirel University)

Abstract

The anticipation of flooding is crucial for River Engineering. The Muskingum technique is among the most renowned investigations on this topic. There are many versions of this model. This work calibrated the parameters of the existing linear Muskingum technique using the Grey Wolf Optimization (GWO) algorithm and Particle Swarm Optimization (PSO). The calibrating procedure is developed using two methods. Initially, as documented in the literature, the parameters employed in the computation of the coefficients are calibrated, and these parameters are subsequently utilized in the formulas to determine the coefficients. In the second instance, the coefficients are calibrated directly. The particle and iteration counts utilized for calibrations are modified in the study. The impact of GWO and PSO on this issue is also examined through the analysis of these figures. The study utilized the Mollasani flood case documented in the literature. The Percentage Absolute Error serves as the error metric. The results acquired in this phase are compared. This comparison utilizes the Taylor diagram and Percentage Absolute Error. The Standard Deviation of the results pertaining to the model’s reliability is also analyzed. Thus, it is observed that the GWO and PSO algorithms, which are heuristic optimization methods for parameter estimation, have alleviated complex scenarios and are near-accurate outcomes. GWO yields superior results compared to PSO. Consequently, it is noted that the GWO and PSO algorithms, which are heuristic optimization techniques for parameter estimation, have mitigated complicated scenarios and produced near-accurate results. Also, GWO produces more favorable outcomes than PSO. The error margin of each calibration method is comparable; however, the particle and iteration counts employed varies. This difference leads to a change in the duration necessary for coefficient computations. Instead of finding the coefficients directly, calibrating the formula variables used to find the coefficients is more effective in achieving fast and accurate results.

Suggested Citation

  • Kemal Saplıoğlu, 2025. "Calibration of Linear Muskingum Model Coefficients and Coefficient Parameters Using Grey Wolf and Particle Swarm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(3), pages 999-1014, February.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:3:d:10.1007_s11269-024-04063-9
    DOI: 10.1007/s11269-024-04063-9
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    References listed on IDEAS

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    1. Hadi Norouzi & Jalal Bazargan, 2022. "Calculation of Water Depth during Flood in Rivers using Linear Muskingum Method and Particle Swarm Optimization (PSO) Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4343-4361, September.
    2. Omid Bozorg Haddad & Farzan Hamedi & Hosein Orouji & Maryam Pazoki & Hugo Loáiciga, 2015. "A Re-Parameterized and Improved Nonlinear Muskingum Model for Flood Routing," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3419-3440, July.
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    4. 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.
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    7. A. Moridi & J. Yazdi, 2017. "Optimal Allocation of Flood Control Capacity for Multi-Reservoir Systems Using Multi-Objective Optimization Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(14), pages 4521-4538, November.
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