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.

General Review of Calibration Process of Nonlinear Muskingum Model and Its Optimization by Up-to-Date Methods

2020 ◽  
pp. 61-83
Author(s):  
Umut Kırdemir ◽  
Umut Okkan
Keyword(s):  
Water Cycle ◽  
Search Algorithm ◽  
Flood Routing ◽  
Flower Pollination Algorithm ◽  
Model Parameters ◽  
Flower Pollination ◽  
Muskingum Model ◽  
Colliding Bodies Optimization ◽  
Nonlinear Muskingum Model ◽  
Outflow Hydrograph

Nonlinear Muskingum method is a very efficient tool in flood routing implementation. It is possible to estimate an outflow hydrograph by a given inflow hydrograph of a flood at a specific point of the river channel. However, it turns out an optimization problem at the stage of employing this method, and it becomes important to reach the optimal model parameters so as to obtain precise outflow hydrograph estimations. Hence, it was decided to utilize five up-to-date optimization algorithms, namely, vortex search algorithm (VSA), gases brownian motion algorithm (GBMO), water cycle algorithm (WCA), flower pollination algorithm (FPA), and colliding bodies optimization (CBO). The algorithms were integrated with the nonlinear Muskingum model so as to estimate the outflow hydrograph of Wilson data, and it was deduced that WCA, FPA, and VSA perform relatively better than the models employed in the other researches before.

scholarly journals A new modified nonlinear Muskingum model and its parameter estimation using the adaptive genetic algorithm

2016 ◽  
Vol 48 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Song Zhang ◽  
Ling Kang ◽  
Liwei Zhou ◽  
Xiaoming Guo
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Mathematical Optimization ◽  
Optimization Procedure ◽  
Flood Routing ◽  
Adaptive Genetic Algorithm ◽  
Muskingum Model ◽  
Elite Strategy ◽  
Data Application ◽  
Different Characteristics

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.

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

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1415 ◽  
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.

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

Calibration of Modular Reconfigurable Robots Based on a Hybrid Search Method

2010 ◽  
Vol 132 (6) ◽  
Author(s):  
Yu Lin ◽  
Fengfeng Xi ◽  
Richard Phillip Mohamed ◽  
Xiao-wei Tu
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Configuration Space ◽  
Global Solutions ◽  
Search Method ◽  
Hybrid Search ◽  
Reconfigurable Robots ◽  
Genetic Algorithm Method

Developed in this paper is a hybrid method for calibration of modular reconfigurable robots (MRRs). The underlying problem under study is unique to MRRs, that is, how to calibrate a set of MRR’s geometric parameters that are applicable to all feasible configurations. For this reason, a hybrid search method is developed to ensure a global search over the MRRs’ workspace for each feasible configuration. By combining a genetic algorithm method with a Monte Carlo method, this method includes three levels of search, namely, pose, workspace, and configuration-space. The final set of global solutions is generated progressively from the results of these three levels of search. The effectiveness of this method is demonstrated through a case study.

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