Parameter estimation of an improved nonlinear Muskingum model using a new hybrid method

2016 ◽  
Vol 48 (5) ◽  
pp. 1253-1267 ◽  
Author(s):  
Majid Niazkar ◽  
Seied Hosein Afzali
Keyword(s):  
Parameter Estimation ◽  
Evaluation Criteria ◽  
Variable Parameter ◽  
Optimization Techniques ◽  
Flood Routing ◽  
Gradient Algorithm ◽  
Hybrid Technique ◽  
Muskingum Model ◽  
Reduced Gradient ◽  
Generalized Reduced Gradient

Although various techniques have been proposed to estimate the parameters of different versions of the Muskingum model, more rigorous techniques and models are still required to improve the computational precision of the calibration process. In this research, a new hybrid technique was proposed for Muskingum parameter estimation. Based on the conducted comprehensive literature review on the Muskingum flood routing models, a new improved Muskingum model with nine constant parameters was presented. Since the inflow-weighted parameter in the proposed model is a function of inflow hydrograph, it varies during the flood period and consequently can also be considered as a variable-parameter Muskingum model. The new hybrid technique was successfully applied for parameter estimation of the new version of Muskingum model for two case studies selected from the literature. Results were compared with those of other methods using several common performance evaluation criteria. The new Muskingum model significantly reduces the sum of the square of the deviations between the observed and routed outflows (SSQ) value for the double-peak case study. Finally, the obtained results indicate that not only the hybrid modified honey bee mating optimization-generalized reduced gradient algorithm somehow overcomes the shortcomings of both zero and first-order optimization techniques, but also the new Muskingum model appears to be the most reliable Muskingum version compared with the other methods considered in this study.

Optimal Design of Semisubmersible’s Form Based on Systems Analysis

1984 ◽  
Vol 106 (4) ◽  
pp. 524-530 ◽  
Author(s):  
S. Akagi ◽  
R. Yokoyama ◽  
K. Ito
Keyword(s):  
Optimal Design ◽  
Computer Aided Design ◽  
Optimization Problem ◽  
Design Method ◽  
Gradient Algorithm ◽  
Interpretive Structural Modeling ◽  
Reduced Gradient ◽  
Optimal Design Method ◽  
Generalized Reduced Gradient ◽  
Aided Design

With the objective of developing a computer-aided design method to seek the optimal semisubmersible’s form, hierarchical relationships among many design objectives and conditions are investigated first based on the interpretive structural modeling method. Then, an optimal design method is formulated as a nonlinear multiobjective optimization problem by adopting three mutually conflicting design objectives. A set of Pareto optimal solutions is derived numerically by adopting the generalized reduced gradient algorithm, and it is ascertained that the designer can determine the optimal form more rationally by investigating the trade-off relationships among design objectives.

A model for fi nding the optimal routes for a combined route container train

Innotrans ◽  
2021 ◽  
pp. 15-21
Author(s):  
Chang Hao ◽  
◽  
Daria Ivanovna Kochneva ◽  
Keyword(s):  
Software Implementation ◽  
Gradient Algorithm ◽  
Rolling Stock ◽  
Optimal Route ◽  
Delivery Time ◽  
Container Transportation ◽  
Cargo Handling ◽  
Reduced Gradient ◽  
Generalized Reduced Gradient ◽  
Train Loading

The article is devoted to the development of the model for finding an optimal route for a combined route container train (CRCT), i.e. a train with a designated route and schedule, en route from the initial to the final station without reforming of a rolling stock, but carrying out cargo handling operations for loading/unloading containers at intermediate stops of the route. It is proposed to call the optimal route of a CRCT, which provides the minimum delivery time while ensuring the targeted train loading on each section and with a set value of demand for container transportation at each point of the route. A software implementation of the model in the MS Excel environment is proposed using the built-in generalized reduced gradient algorithm.

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.

Optimization of a Novel Combined Power/Refrigeration Thermodynamic Cycle

2003 ◽  
Vol 125 (2) ◽  
pp. 212-217 ◽  
Author(s):  
Shaoguang Lu ◽  
D. Yogi Goswami
Keyword(s):  
Waste Heat ◽  
Thermodynamic Cycle ◽  
Gradient Algorithm ◽  
Cycle Performance ◽  
Working Fluid ◽  
Heat Sources ◽  
Objective Functions ◽  
Ambient Temperatures ◽  
Reduced Gradient ◽  
Generalized Reduced Gradient

A novel combined power/refrigeration thermodynamic cycle is optimized for thermal performance in this paper. The cycle uses ammonia-water binary mixture as a working fluid and can be driven by various heat sources, such as solar, geothermal, and low temperature waste heat. The optimization program, which is based on the Generalized Reduced Gradient algorithm, can be used to optimize for different objective functions. In addition, cycle performance over a range of ambient temperatures was investigated.

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.

Optimal Design of a Mechanism Used for Opening and Shutting a Ship’s Hatch Cover

1984 ◽  
Vol 106 (4) ◽  
pp. 503-509
Author(s):  
Koichi Ito ◽  
Tadashi Kuroiwa ◽  
Shinsuke Akagi
Keyword(s):  
Optimal Design ◽  
Nonlinear Optimization ◽  
Optimization Problem ◽  
Numerical Study ◽  
Optimal Solution ◽  
Optimization Method ◽  
Gradient Algorithm ◽  
Linkage Mechanism ◽  
Reduced Gradient ◽  
Generalized Reduced Gradient

A nonlinear optimization method is proposed to design a linkage mechanism used for opening and shutting a ship’s hatch cover. Considering the maximum force of the oil cylinder necessary to move the hatch cover as the objective function to be minimized, the design problem to determine the optimal configuration of linkage mechanism is formulated as a nonlinear optimization problem of minimax type. It it shown that the optimal solution can be derived by adopting the generalized reduced gradient algorithm together with a linkage statical simulation model, and the effectiveness of the algorithm is ascertained through a numerical study.

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