Enhancing Flood Routing Accuracy: A Fuzzified Approach to Nonlinear Variable-Parameter Muskingum Model
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11269-024-03846-4.pdf
Reference37 articles.
1. Akbari R, Hessami-Kermani M-R (2023) A new method for dividing flood period in the variable-parameter Muskingum models. Hydrol Res 53:241–257. https://doi.org/10.2166/nh.2021.192
2. Akbari R, Hessami-Kermani M-R, Shojaee S (2020) Flood routing: improving outflow using a new nonlinear Muskingum model with four variable parameters coupled with PSO-GA algorithm. Water Resour Manag 34:3291–3316. https://doi.org/10.1007/s11269-020-02613-5
3. Al-Bedyry N, Mergan M, Rasheed M, Al-Khafaji Z, Al-Husseinawi FN (2023) The use of genetic expression programming to optimize the parameters of the Muskingum method comparison with numerical methods, Euphrates river a case study. Arch Civ Eng 2023:507–519. https://doi.org/10.24425/ace.2023.146094
4. Atashi V, Barati R, Lim YH (2023) Improved river flood routing with spatially variable exponent Muskingum model and sine cosine optimization algorithm. Environ Process 10(3):42. https://doi.org/10.1007/s40710-023-00658-3
5. Ayvaz MT, Gurarslan G (2017) A new partitioning approach for nonlinear Muskingum flood routing models with lateral flow contribution. J Hydrol 553:142–159. https://doi.org/10.1016/j.jhydrol.2017.07.050
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