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Investigation the Effect of Using Variable Values for the Parameters of the Linear Muskingum Method Using the Particle Swarm Algorithm (PSO)

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  • Jalal Bazargan

    (University of Zanjan)

  • Hadi Norouzi

    (University of Zanjan)

Abstract

Flood routing is a technique to determine the flood hydrograph at a point of downstream where is of great importance and flood-induced risks can cause irreparable damages. Routing methods can be classified into two categories: hydraulic routing and hydrologic routing. Hydrologic methods are less accurate than hydraulic methods but they are widely used for engineering of rivers due to simplicity and being acceptable. Muskingum is a simple, widely used hydrologic method in the flood routing. In present study, accuracy of the linear Muskingum method has been evaluated using the Particle Swarm Optimization (PSO) algorithm in a Karun River reach bounded to the Mollasani hydrometric station and Ahwaz station upstream and downstream of the river, respectively. The results suggest that if three distinct values rather than constant values are used for X, K, ∆푡 parameters in the Muskingum method, the accuracy of computed outflow will be increased particularly in the peak section of hydrograph so that the Mean Relative Error (MRE) of the peak hydrograph section was 2.44% when constants were. However, in the case of using three different values for these parameters, the error value reached 0.89%.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:14:d:10.1007_s11269-018-2082-6
    DOI: 10.1007/s11269-018-2082-6
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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. 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.
    4. 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.
    5. Jasem Al-Humoud & Ismail Esen, 2006. "Approximate Methods for the Estimation of Muskingum Flood Routing Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(6), pages 979-990, December.
    6. 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.
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    Cited by:

    1. Bing Yan & Yicheng Gu & En Li & Yi Xu & Lingling Ni, 2024. "Runoff Prediction of Tunxi Basin under Projected Climate Changes Based on Lumped Hydrological Models with Various Model Parameter Optimization Strategies," Sustainability, MDPI, vol. 16(16), pages 1-21, August.
    2. 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.
    3. 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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