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Flood Routing: Improving Outflow Using a New Non-linear Muskingum Model with Four Variable Parameters Coupled with PSO-GA Algorithm

Author

Listed:
  • Reyhaneh Akbari

    (Shahid Bahonar University of Kerman)

  • Masoud-Reza Hessami-Kermani

    (Shahid Bahonar University of Kerman)

  • Saeed Shojaee

    (Shahid Bahonar University of Kerman)

Abstract

Flood is one of the most destructive natural disasters that damages people’s lives dramatically. Thus, it is crucial for researchers and politicians to research flood routing. The non-linear Muskingum model has been significantly considered by engineers and researchers in flood routing. In this study, in order to increase the accuracy of outflow prediction, the new non-linear Muskingum model, with four variable parameters, is proposed for the first time. In the proposed model, the inflows are divided into three sub-regions, and each of the four hydrologic parameters has a various value in each sub-region. How to select the sub-regions, as well as the values of the hydrologic parameters, is determined by combining both the Particle Swarm Optimization and Genetic Algorithm. The proposed model is studied in four case studies. Compared to the non-linear Muskingum model with three parameters, the amount of sum squared deviation (SSQ) decreased 52 and 6.9% for the first and second case studies, respectively. Compared to the best variable parameter model, the SSQ for the third and fourth case studies reduced 76 and 62%, respectively. The results showed that the SSQ was considerably decreased significantly in all of the four case studies, and the proposed model has superiority over other non-linear Muskingum models, which have been used by other researchers so far.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:10:d:10.1007_s11269-020-02613-5
    DOI: 10.1007/s11269-020-02613-5
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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. Mehdi Nikoo & Fatemeh Ramezani & Marijana Hadzima-Nyarko & Emmanuel Karlo Nyarko & Mohammad Nikoo, 2016. "Flood-routing modeling with neural network optimized by social-based algorithm," 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. 82(1), pages 1-24, May.
    4. 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.
    5. 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.
    6. Garg, Harish, 2016. "A hybrid PSO-GA algorithm for constrained optimization problems," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 292-305.
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    Cited by:

    1. 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.
    2. 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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