Automatic Calibration of a Hydrological Model Using Multiobjective Particle Swarm Optimization and TOPSIS

Zhang, Lei; Cui, Guangbai

doi:10.1109/csie.2009.802

Cited by 2 publications

(4 citation statements)

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“…A method which combines a genetic algorithm with TOPSIS for the Xinanjiang model is presented to handle the multicriteria parameter calibration problem with a single procedure [37]. A MOPSO algorithm and an entropy-based TOPSIS ranking method were employed to calibrate and verify the Xinanjiang model in Misai catchment [38].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this paper, a TOPSIS method is employed to rank the nondominated solutions, and the entropy theory of information is also adapted to assign objective weights reasonably for different attributes. e entropy-based TOPSIS procedure consists of the following steps [32,38]:…”

Section: 1mentioning

confidence: 99%

“…Generally, the weights of attributes are determined from the experience of decision maker depending on the specific problem. For more accuracy, a reasonable method based on the theory of information entropy for assigning objective weights of attributes is adapted to calculate objective weights [38]. According to the definition of entropy, the entropy value of each attribute (objective) j can be measured as follows:…”

Section: 1mentioning

confidence: 99%

“…us, we adopt the method of calculating the information entropy of each objective function in the optimal solutions to determine their weights for more accuracy [38]. Table 6 indicates the entropy value and weights of 19 functions which were calculated according to equations (29) and (31).…”

Section: Number Of Optimal Solutionsmentioning

confidence: 99%

See 3 more Smart Citations

Evaluation Method of Multiobjective Functions’ Combination and Its Application in Hydrological Model Evaluation

Huo

Liu

2020

Computational Intelligence and Neuroscience

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Parameter optimization of a hydrological model is intrinsically a high dimensional, nonlinear, multivariable, combinatorial optimization problem which involves a set of different objectives. Currently, the assessment of optimization results for the hydrological model is usually made through calculations and comparisons of objective function values of simulated and observed variables. Thus, the proper selection of objective functions’ combination for model parameter optimization has an important impact on the hydrological forecasting. There exist various objective functions, and how to analyze and evaluate the objective function combinations for selecting the optimal parameters has not been studied in depth. Therefore, to select the proper objective function combination which can balance the trade-off among various design objectives and achieve the overall best benefit, a simple and convenient framework for the comparison of the influence of different objective function combinations on the optimization results is urgently needed. In this paper, various objective functions related to parameters optimization of hydrological models were collected from the literature and constructed to nine combinations. Then, a selection and evaluation framework of objective functions is proposed for hydrological model parameter optimization, in which a multiobjective artificial bee colony algorithm named RMOABC is employed to optimize the hydrological model and obtain the Pareto optimal solutions. The parameter optimization problem of the Xinanjiang hydrological model was taken as the application case for long-term runoff prediction in the Heihe River basin. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) based on the entropy theory is adapted to sort the Pareto optimal solutions to compare these combinations of objective functions and obtain the comprehensive optimal objective functions’ combination. The experiments results demonstrate that the combination 2 of objective functions can provide more comprehensive and reliable dominant options (i.e., parameter sets) for practical hydrological forecasting in the study area. The entropy-based method has been proved that it is effective to analyze and evaluate the performance of different combinations of objective functions and can provide more comprehensive and impersonal decision support for hydrological forecasting.

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Section: Literature Reviewmentioning

confidence: 99%

Section: 1mentioning

confidence: 99%

Section: 1mentioning

confidence: 99%