River Flood Routing Using the Multi-Reach Linear Muskingum Approach and Marin Predators Algorithm

Document Type : Research Paper

Authors

Water Engineering Department, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran

Abstract

Flood routing is an essential and fundamental issue in water resources management and flood control engineering. The Muskingum model is one of the well-known and the most widely used hydrological flood routing approaches. In addition to reasonable accuracy, the linear Muskingum model is also simpler and has a lower cost than that of hydraulic and nonlinear Muskingum models. In this study, a multi-reach linear Muskingum method considering lateral flow is proposed to increase the accuracy and efficiency of the current version of the Muskingum model. To the aim, the river path is divided into a finite number of sub-intervals, and the Muskingum model is then separately applied to each sub-interval successively; in such a way that the input flood hydrograph for each sub-interval is indeed the same as the output flood hydrograph from Muskingum calculations in the previous sub-interval. Here, besides the parameters  and ,  as lateral flow coefficient and  as the number of sub-intervals are also considered as decision variables where the Marine Predators Algorithm (MPA) was used to determine their optimized values. The results showed that the multi-reach approach increased the accuracy of the sum of squared deviation (SSQ) by 70 and 73 percent for Wilson data and Wye river flood, respectively, indicating a higher accuracy for the multi-reach version Muskingum compared to that of single-reach.In addition, the multi-reach Muskingum approach was tested on three flood events of Karun river, in which the calculated statistical criteria, all, show a high accuracy for the proposed method and the MPA.

Keywords

References

Akan, O. (2006). Open Channel Hydraulics. In Open Channel Hydraulics. https://doi.org/10.1016/B978-0-7506-6857-6.X5000-0
Ayvaz, M. T., & Gurarslan, G. (2017). A new partitioning approach for nonlinear Muskingum flood routing models with lateral flow contribution. Journal of Hydrology, 553, 142–159. https://doi.org/10.1016/j.jhydrol.2017.07.050
Barati, R. (2013). Closure to “Parameter Estimation of Nonlinear Muskingum Models Using Nelder-Mead Simplex Algorithm” by Reza Barati. Journal of Hydrologic Engineering, 18(3), 367–370.
Bayrami, M., Vatankhah, A., & Nazi Ghameshlou, A. (2019). Flood Routing using Muskingum Model with Fractional Derivative. Iranian Journal of Soil and Water Research, 50(7), 1667–1676.  (In Farsi) https://doi.org/10.22059/ijswr.2019.275566.668120
Bazargan, J., & Norouzi, H. (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, 32(14), 4763–4777. https://doi.org/10.1007/s11269-018-2082-6
Chagas, P., & Souza, R. (2005). Solution of Sanin Venant’s Equation to Study Flood in Rivers, through Numerical Methods. Hydrology Days, 55, 205–210.
Cunge, J. A. (1969). On the subject of a flood propagation computation method (musklngum method). Journal of Hydraulic Research, 7(2), 205–230. https://doi.org/10.1080/00221686909500264
Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A. H. (2020). Marine Predators Algorithm: A nature-inspired metaheuristic. Expert Systems with Applications, 152, 113377. https://doi.org/10.1016/j.eswa.2020.113377
Hadi Norouzi and Jalal bazargan. (2020). Flood routing by linear Muskingum method using two basic fl oods data using particle swarm optimization (PSO) algorithm. 1897–1908. https://doi.org/10.2166/ws.2020.099
Hasanpour, F., & Sheykhalipour, Z. (2014). Comparison of the Artificial Intelligence Techniques and the Muskingum Methods in Flood Routing Estimation. (In Farsi)
Hosseini, S. M. (2009). Application of spreadsheets in developing flexible multiple-reach and multiple-branch methods of Muskingum flood routing. Computer Applications in Engineering Education, 17(4), 448–454. https://doi.org/10.1002/cae.20234
Karahan, H., Gurarslan, G., & Geem, Z. W. (2013). Parameter Estimation of the Nonlinear Muskingum Flood-Routing Model Using a Hybrid Harmony Search Algorithm. Journal of Hydrologic Engineering, 18(3), 352–360. https://doi.org/10.1061/(asce)he.1943-5584.0000608
Kim, J. H., Geem, Z. W., & Kim, E. S. (2001). Parameter estimation of the nonlinear muskingum model using harmony search 1. JAWRA Journal of the American Water Resources Association, 37(5), 1131–1138.
Mahmoudinia, S., Javan, M., & Eghbalzade, A. (2014). Comparison of different objective function on estimation of linear and non-linear Muskingum model optimum parameters. WEJ, 7(20), 29–42. http://wej.miau.ac.ir/article%7B%5C_%7D498.html
Mohammad Rezapour Tabari, M., & Emami Dehcheshmeh, S. A. (2018). Development of Nonlinear Muskingum Model Using Evolutionary Algorithms Hybrid. Iran-Water Resources Research, 14(1), 160–167. http://iwrr.sinaweb.net/article%7B%5C_%7D51333.html
Mohan, S. (1997). Parameter estimation of nonlinear Muskingum models using genetic algorithm. Journal of Hydraulic Engineering, 123(2), 137–142.
Norouzi, H., & Bazargan, J. (2020). Flood routing by linear Muskingum method using two basic floods data using particle swarm optimization (PSO) algorithm. Water Supply, 20(5), 1897–1908.
O’donnell, T. (1985). A direct three-parameter Muskingum procedure incorporating lateral inflow. Hydrological Sciences Journal, 30(4), 479–496.
Oladghaffari, A., Fakheri-Fard, A., Nazemi, A. H., & Ghorbani, M. A. (2010). Hydraulic Flood Routing Using Dynamic Wave Method and Comparison with Linear and Nonlinear Hydrologic Muskingum Routing Methods (Case Study: Lighvan-Chai). Water and Soil Science, 20(3), 47–60. https://water-soil.tabrizu.ac.ir/article%7B%5C_%7D1331.html
Ponce, V. M., & Lugo, A. (2001). Modeling looped ratings in Muskingum-Cunge routing. Journal of Hydrologic Engineering, 6(2), 119–124.
Rahmani, M. (2017). Comparative evaluation of linear and nonlinear hydrological methods for river flood routing. University of Lamali Gorgani, Gorgan.
Samani HMV, S. G. (2004). Hydrologic flood routing in branched river systems via nonlinear optimization. Journal of Hydraulical Researches, 24(1), 55–59.
Swief, R. A., Hassan, N. M., Hasanien, H. M., Abdelaziz, A. Y., & Kamh, M. Z. (2021). Multi-Regional Optimal Power Flow Using Marine Predators Algorithm Considering Load and Generation Variability. IEEE Access, 9, 74600–74613. https://doi.org/10.1109/access.2021.3081374
Vijay P. Singh. (1988). hydrologic systems.
Yang, W., Wang, J., Sui, J., Zhang, F., & Zhang, B. (2019). A Modified Muskingum Flow Routing Model for Flood Wave Propagation during River Ice Thawing-Breakup Period.
Yoo, C., Lee, J., & Lee, M. (n.d.). Parameter Estimation of the Muskingum Channel Flood-Routing Model in Ungauged Channel Reaches. 1–9. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001507.
Yoon, B. J., & Padmanabhan, G. (1994). P a r a m e t e r e s t i m a t i o n of l i n e a r and nonlinear muskingum models. 119(5), 600–610.
 
 
Iranian Journal of Soil and Water Research
Volume 52, Issue 10
January 2022
Pages 2693-2707

Files

Share

How to cite

Statistics