Evaluation of nonlinear Muskingum model with continuous and discontinuous exponent parameters

2015 ◽  
Vol 19 (7) ◽  
pp. 2281-2290 ◽  
Author(s):  
Said M. Easa
Keyword(s):  
Muskingum Model ◽  
Nonlinear Muskingum Model

Parameter Identification of Nonlinear Muskingum Model with Backtracking Search Algorithm

2016 ◽  
Vol 30 (8) ◽  
pp. 2767-2783 ◽  
Author(s):  
Xiaohui Yuan ◽  
Xiaotao Wu ◽  
Hao Tian ◽  
Yanbin Yuan ◽  
Rana Muhammad Adnan
Keyword(s):  
Parameter Identification ◽  
Search Algorithm ◽  
Backtracking Search ◽  
Backtracking Search Algorithm ◽  
Muskingum Model ◽  
Nonlinear Muskingum Model

scholarly journals Optimization of the Nonlinear Muskingum Model Parameters for the River Routing, Tigris River a Case Study

2021 ◽  
Vol 16 (6) ◽  
pp. 649-656
Author(s):  
Maher Abd Ameer Kadim ◽  
Isam Issa Omran ◽  
Alaa Ali Salman Al-Taai
Keyword(s):  
Genetic Algorithm ◽  
Least Squares Method ◽  
Harmony Search ◽  
Flood Routing ◽  
Model Parameters ◽  
Muskingum Model ◽  
Tigris River ◽  
Nonlinear Muskingum Model ◽  
Genetic Algorithm Method

Flood forecasting and management are one of the most important strategies necessary for water resource and decision planners in combating flood problems. The Muskingum model is one of the most popular and widely used applications for the purpose of predicting flood routing. The particle swarm optimization (PSO) methodology was used to estimate the coefficients of the nonlinear Muskingum model in this study, comparing the results with the methods of genetic algorithm (GA), harmony search (HS), least-squares method (LSM), and Hook-Jeeves (HJ). The average monthly inflow for the Tigris River upstream at the Al-Mosul dam was selected as a case study for estimating the Muskingum model's parameters. The analytical and statistical results showed that the PSO method is the best application and corresponds to the results of the Muskingum model, followed by the genetic algorithm method, according to the following general descending sequence: PSO, GA, LSM, HJ, HS. The PSO method is characterized by its accurate results and does not require many assumptions and conditions for its application, which facilitates its use a lot in the subject of hydrology. Therefore, it is better to recommend further research in the use of this method in the implementation of future studies and applications.

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

2015 ◽  
Vol 29 (9) ◽  
pp. 3419-3440 ◽  
Author(s):  
Omid Bozorg Haddad ◽  
Farzan Hamedi ◽  
Hosein Orouji ◽  
Maryam Pazoki ◽  
Hugo A. Loáiciga
Keyword(s):  
Flood Routing ◽  
Muskingum Model ◽  
Nonlinear Muskingum Model

scholarly journals A Class of Parameter Estimation Methods for Nonlinear Muskingum Model Using Hybrid Invasive Weed Optimization Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Aijia Ouyang ◽  
Li-Bin Liu ◽  
Zhou Sheng ◽  
Fan Wu
Keyword(s):  
Optimization Problem ◽  
Estimation Methods ◽  
Invasive Weed Optimization ◽  
Third Order ◽  
Invasive Weed ◽  
Muskingum Model ◽  
Unconstrained Optimization Problem ◽  
Discretization Schemes ◽  
Nonlinear Muskingum Model ◽  
Parameter Estimation Methods

Nonlinear Muskingum models are important tools in hydrological forecasting. In this paper, we have come up with a class of new discretization schemes including a parameterθto approximate the nonlinear Muskingum model based on general trapezoid formulas. The accuracy of these schemes is second order, ifθ≠1/3, but interestingly whenθ=1/3, the accuracy of the presented scheme gets improved to third order. Then, the present schemes are transformed into an unconstrained optimization problem which can be solved by a hybrid invasive weed optimization (HIWO) algorithm. Finally, a numerical example is provided to illustrate the effectiveness of the present methods. The numerical results substantiate the fact that the presented methods have better precision in estimating the parameters of nonlinear Muskingum models.

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