A Re-Parameterized and Improved Nonlinear Muskingum Model for Flood Routing

Bozorg‐Haddad, Omid; Hamedi, Farzan; Orouji, Hossein; Pazoki, Maryam; Loáiciga, Hugo A.

doi:10.1007/s11269-015-1008-9

Cited by 37 publications

(13 citation statements)

References 48 publications

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“…The Muskingum routing method has been improved by many researchers since it was proposed in 1939 (Koussis, 2009;McCarthy, 1939). Most of these theoretical studies on the Muskingum method include model validation (Afzali and Niazkar, 2015), analysis of model structure, model structure improvement (Haddad et al, 2015), and calibration of model parameters by considering the temporally varying factors.…”

Section: Introductionmentioning

confidence: 99%

A bivariable coupling model for river channel routing developed from the flow continuity equation and its application

Bao

Cui

et al. 2019

WSA

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In this study, a bivariable coupling model for river channel routing is presented. The proposed model is developed from the Priessmann 4-point implicit differential scheme with a weight coefficient of river flow continuity equation. It is based on the transformation of two different expression forms of river channel storage equation. Furthermore, we consider the impact of lateral inflow along the study river channel from another perspective. In this paper we deduct lateral inflow from the lower section instead of adding lateral inflow to the upper section. In order to be representative of geographical range, river channel characteristics, flood magnitude, hydraulic characteristics and time, the proposed model is tested in 38 river channels of 6 river systems in China by using observed data during flood season. The rationality of model structure and the validity of model simulation are examined comprehensively. Comparison between the proposed model and Muskingum model shows that the proposed model can improve the simulation accuracy. The results show that the simulation accuracy and stability of the bivariable coupling model is much better than that of the Muskingum model.

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Section: Introductionmentioning

confidence: 99%

A bivariable coupling model for river channel routing developed from the flow continuity equation and its application

Bao

Cui

et al. 2019

WSA

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“…This model used the cuckoo algorithm and predicted peak discharge more accurately than other models. The shuffled frog leaping algorithm (SFLA) based on a three-parameter Muskingum model was used for flood routing [25]. Results showed that the SSD and SAD values for the SFLA were lower than those of the GA, PSO and nonlinear programming methods.…”

Section: Introductionmentioning

confidence: 99%

Improving the Muskingum Flood Routing Method Using a Hybrid of Particle Swarm Optimization and Bat Algorithm

Ehteram

Othman

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2018

Water

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Flood prediction and control are among the major tools for decision makers and water resources planners to avoid flood disasters. The Muskingum model is one of the most widely used methods for flood routing prediction. The Muskingum model contains four parameters that must be determined for accurate flood routing. In this context, an optimization process that self-searches for the optimal values of these four parameters might improve the traditional Muskingum model. In this study, a hybrid of the bat algorithm (BA) and the particle swarm optimization (PSO) algorithm, i.e., the hybrid bat-swarm algorithm (HBSA), was developed for the optimal determination of these four parameters. Data for the three different case studies from the USA and the UK were utilized to examine the suitability of the proposed HBSA for flood routing. Comparative analyses based on the sum of squared deviations (SSD), sum of absolute deviations (SAD), error of peak discharge, and error of time to peak showed that the proposed HBSA based on the Muskingum model achieved excellent flood routing accuracy compared to that of other methods while requiring less computational time.

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“…The original paper estimated the Muskingum parameters by posing them as unknown variables. An initial condition is needed to estimate the parameter S 0 (the initial river-reach storage), and for this purpose, the initial condition I 0 ¼ O 0 is applied in the flood routing, in which I 0 denotes the initial reach inflow and O 0 denotes the initial calculated outflow (e.g., Chow 1959;Das 2007;Chu and Chang 2009;Orouji et al 2012;Easa 2013;Hamedi et al 2014;Bozorg-Haddad et al 2015a). The authors intended to improve the initial condition to produce a well-calibrated Muskingum model in the original paper.…”

mentioning

confidence: 99%

“…The discussers compared the results of the NL4 Muskingum model with those of the NL5-1 and NL5-2 models. These three models employ optimization methods that include the Excel Solver, the shuffled frog leaping algorithm (SFLA), and the Nelder-Mead simplex (NMS) (Bozorg-Haddad et al 2015a). The results of the evolutionary and metaheuristic algorithms must be compared in terms of the number of functional evaluations required for convergence (Solgi et al 2017).…”

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confidence: 99%

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Closure to “Parameter Estimation of Extended Nonlinear Muskingum Models with the Weed Optimization Algorithm” by Farzan Hamedi, Omid Bozorg-Haddad, Maryam Pazoki, Hamid-Reza Asgari, Mehran Parsa, and Hugo A. Loáiciga

Asgari

Bozorg‐Haddad

Loáiciga

2018

J. Irrig. Drain Eng.

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A Re-Parameterized and Improved Nonlinear Muskingum Model for Flood Routing

Cited by 37 publications

References 48 publications

A bivariable coupling model for river channel routing developed from the flow continuity equation and its application

A bivariable coupling model for river channel routing developed from the flow continuity equation and its application

Improving the Muskingum Flood Routing Method Using a Hybrid of Particle Swarm Optimization and Bat Algorithm

Closure to “Parameter Estimation of Extended Nonlinear Muskingum Models with the Weed Optimization Algorithm” by Farzan Hamedi, Omid Bozorg-Haddad, Maryam Pazoki, Hamid-Reza Asgari, Mehran Parsa, and Hugo A. Loáiciga

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