Have a personal or library account? Click to login
Traditional and Numerical Approaches for Estimating Reservoir Routing Parameters Cover

Traditional and Numerical Approaches for Estimating Reservoir Routing Parameters

Open Access
|Nov 2025

References

  1. AKAN A. O.: 2-Energy and momentum principles. Open channel hydraulics. Oxford: Butterworth-Heinemann, 2006, pp. 24–66.
  2. VALIZADEH N. - El-Shafie A. : Forecasting the level of reservoirs using multiple input fuzzification in ANFIS. Water Resour. Manag., vol. 27, no. 9, 2013, pp. 3319–3331.
  3. AHMED J.A. - A. Sarma K. : Artificial neural network model for synthetic streamflow generation. Water Resour. Manag., vol. 21, no. 6, 2007, pp. 1015–1029.
  4. KISI O., et al.: Forecasting daily lake levels using artificial intelligence approaches. Comput. Geosci., vol. 41, 2012, pp. 169–180.
  5. OGBONNA D., et al.: Application of Flood Routing Model for Flood Mitigation in Orashi River, South-East Nigeria. J. Geosci. Environ. Prot., vol. 5, no. 03, 2017, p. 31.
  6. BAGATUR T.- Onen F.: Development of predictive model for flood routing using genetic expression programming. J. Flood Risk Manag., vol. 11, 2018, pp. S444–S454.
  7. ZHOU Y., et al.: Methodology that improves water utilization and hydropower generation without increasing flood risk in mega cascade reservoirs. Energy, vol. 143,2018, pp. 785–796.
  8. KADHIM M.A., et al.: Evaluation of Flood Routing Models and Their Relationship to The Hydraulic Properties of the Diyala River Bed. in IOP Conference Series: Earth and Environmental Science, IOP Publishing, 2022, p. 12058.
  9. HAMEDI F., et al. : Parameter estimation of extended nonlinear Muskingum models with the weed optimization algorithm. J. Irrig. Drain. Eng., vol. 142, no. 12, 2016, p. 4016059.
  10. BARATI R., et al. :Discussions and Closures. J. Irrig. Drain Eng, vol. 144, no. 1, 2018, p. 7017021.
  11. Reggiani P., et al. : On mass and momentum conservation in the variable-parameter Muskingum method. J. Hydrol., vol. 543, 2016, pp. 562–576.
  12. YOO C., et al. : Parameter estimation of the Muskingum channel flood-routing model in ungauged channel reaches. J. Hydrol. Eng., vol. 22, no. 7, 2017, p. 5017005.
  13. NIAZKAR M. - Afzali S. H. : New nonlinear variable-parameter Muskingum models. KSCE J. Civ. Eng., vol. 21, no. 7, 2017, pp. 2958–2967.
  14. MAZZOLENI M., et al.: Real-time assimilation of streamflow observations into a hydrological routing model: effects of model structures and updating methods. Hydrol. Sci. J., vol. 63, no. 3, 2018, pp. 386–407.
  15. VATANKHAH A. R.: Discussion of ‘assessment of modified honey bee mating optimization for parameter estimation of nonlinear muskingum models’ by Majid Niazkar and Seied Hosein Afzali. J. Hydrol. Eng., vol. 23, no. 4, 2018, p. 7018002.
  16. LEE E. H., et al.: Development and application of advanced Muskingum flood routing model considering continuous flow. Water, vol. 10, no. 6, 2018 p. 760.
  17. LEE E.H., “Development of a New 8-Parameter Muskingum Flood Routing Model with Modified Inflows. Water, vol. 13, no. 22, 2021, p. 3170.
  18. ZHANG S., et al.: Parameter estimation of nonlinear Muskingum model with variable exponent using adaptive genetic algorithm. Environ. Conserv. Clean Water, Air Soil (CleanWAS); IWA Publ. London, UK, 2017, pp. 231–237.
  19. COSTABILE P., et al.: Performances and limitations of the diffusive approximation of the 2-d shallow water equations for flood simulation in urban and rural areas. Appl. Numer. Math., vol. 116, 2017, pp. 141–156.
  20. PERUMAL M., et al.: Evaluation of a physically based quasi-linear and a conceptually based nonlinear Muskingum methods. J. Hydrol., vol. 546, 2017, pp. 437–449.
  21. AKBARIFARD S., et al.: Parameter estimation of the nonlinear muskingum flood-routing model using water cycle algorithm. J. Watershed Manag. Res., vol. 8, no. 16, 2018, pp. 34–43.
  22. CAI X., et al.: Bat algorithm with triangle-flipping strategy for numerical optimization. Int. J. Mach. Learn. Cybern., vol. 9, no. 2, 2018, pp. 199–215.
  23. HADDAD O.B., et al.: Application of a hybrid optimization method in Muskingum parameter estimation. J. Irrig. Drain. Eng., vol. 141, no. 12, 2015, p. 4015026.
  24. WANG L., et al.: Accuracy of the Muskingum-Cunge method for constant-parameter diffusion-wave channel routing with lateral inflow. arXiv Prepr. arXiv1802.04429, 2018.
  25. JAMIL M., et al.: Multimodal function optimization using an improved bat algorithm in noise-free and noisy environments. in Nature-Inspired Computing and Optimization, Springer, 2017, pp. 29–49.
  26. NIAZKAR M. - Afzali S.H. : Assessment of modified honey bee mating optimization for parameter estimation of nonlinear Muskingum models. J. Hydrol. Eng., vol. 20, no. 4, 2015, p. 4014055.
  27. MOGHADDAM A., et al.: Parameters estimation for the new four-parameter nonlinear Muskingum model using the particle swarm optimization. Water Resour. Manag., vol. 30, no. 7, 2016, pp. 2143–2160.
  28. KANG L. - Zhang S. : Application of the elitist-mutated PSO and an improved GSA to estimate parameters of linear and nonlinear Muskingum flood routing models. PLoS One, vol. 11, no. 1, 2016, p. e0147338.
  29. AL SHAIKHLI H., et al.: Using Flow 3D Simulation, Multiple Nonlinear Regression Approach, and Artificial Neural Network Approach Approaches to Study the Behavior of Vertical Drop Structures. Civil and Environmental Engineering, Volume 20, Issue 2, (December 2024), pp. 654 - 683.
  30. JASSAM M. G., et al.: Effect of Initial Water Content on the Collapsibility of Natural and Cement-Treated Gypseous Soils. Civil and Environmental Engineering, Volume 19, Issue 2 (December 2023), pp. 610 - 617.
DOI: https://doi.org/10.2478/cee-2025-0100 | Journal eISSN: 2199-6512 | Journal ISSN: 1336-5835
Language: English
Page range: 1375 - 1383
Published on: Nov 12, 2025
Published by: University of Žilina
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2025 Zainab Ali Omran, published by University of Žilina
This work is licensed under the Creative Commons Attribution 4.0 License.