Parameter estimation of a new four-parameter Muskingum flood routing model

First, a novel nonlinear Muskingum flood routing model with a variable exponent parameter and simultaneously considering the lateral flow along the river reach (named VEP-NLMM-L) was developed in this research. Then, an improved real-coded adaptive genetic algorithm (RAGA) with elite strategy was applied for precise parameter estimation of the proposed model. The problem was formulated as a mathematical optimization procedure to minimize the sum of the squared deviations (SSQ) between the observed and the estimated outflows. Finally, the VEP-NLMM-L was validated on three watersheds with different characteristics (Case 1 to 3). Comparisons of the optimal results for the three case studies by traditional Muskingum models and the VEP-NLMM-L show that the modified Muskingum model can produce the most accurate fit to outflow data. Application results in Case 3 also indicate that the VEP-NLMM-L may be suitable for solving river flood routing problems in both model calibration and prediction stages.

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Discussion of “Parameter Estimation of the Nonlinear Muskingum Flood-Routing Model Using a Hybrid Harmony Search Algorithm” by Halil Karahan, Gurhan Gurarslan, and Zong Woo Geem

Journal of Hydrologic Engineering ◽

10.1061/(asce)he.1943-5584.0000897 ◽

2014 ◽

Vol 19 (4) ◽

pp. 842-845 ◽

Cited By ~ 3

Author(s):

Reza Barati

Keyword(s):

Parameter Estimation ◽

Search Algorithm ◽

Harmony Search ◽

Harmony Search Algorithm ◽

Flood Routing

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Multi-Objective Parameter Estimation of Improved Muskingum Model by Wolf Pack Algorithm and Its Application in Upper Hanjiang River, China

Water ◽

10.3390/w10101415 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1415 ◽

Cited By ~ 4

Author(s):

Tao Bai ◽

Jian Wei ◽

Wangwang Yang ◽

Qiang Huang

Keyword(s):

Parameter Estimation ◽

Flood Routing ◽

Practical Significance ◽

Absolute Difference ◽

Model Parameters ◽

Multi Objective ◽

Muskingum Model ◽

Efficient Alternative ◽

Hanjiang River ◽

Swarm Intelligence Optimization

In order to overcome the problems in the parameter estimation of the Muskingum model, this paper introduces a new swarm intelligence optimization algorithm—Wolf Pack Algorithm (WPA). A new multi-objective function is designed by considering the weighted sum of absolute difference (SAD) and determination coefficient of the flood process. The WPA, its solving steps of calibration, and the model parameters are designed emphatically based on the basic principle of the algorithm. The performance of this algorithm is compared to the Trial Algorithm (TA) and Particle Swarm Optimization (PSO). Results of the application of these approaches with actual data from the downstream of Ankang River in Hanjiang River indicate that the WPA has a higher precision than other techniques and, thus, the WPA is an efficient alternative technique to estimate the parameters of the Muskingum model. The research results provide a new method for the parameter estimation of the Muskingum model, which is of great practical significance to improving the accuracy of river channel flood routing.

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