Estimation of Nonlinear Muskingum Model Parameter Using Differential Evolution

2012 ◽  
Vol 17 (2) ◽  
pp. 348-353 ◽  
Author(s):  
Dong-Mei Xu ◽  
Lin Qiu ◽  
Shou-Yu Chen
Keyword(s):  
Differential Evolution ◽  
Model Parameter ◽  
Muskingum Model ◽  
Nonlinear Muskingum Model

Utilizing Differential Evolution for an Automated Compact Model Parameter Extraction

2021 ◽  
Author(s):  
Marc Huppmann ◽  
Klaus-Willi Pieper ◽  
Andi Buzo ◽  
Linus Maurer ◽  
Georg Pelz
Keyword(s):  
Differential Evolution ◽  
Parameter Extraction ◽  
Compact Model ◽  
Model Parameter

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.

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