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http://dx.doi.org/10.3741/JKWRA.2007.40.9.677

Optimization of Tank Model Parameters Using Multi-Objective Genetic Algorithm (I): Methodology and Model Formulation  

Kim, Tae-Soon (School of Civil and Environmental Engineering, Yonsei Univ.)
Jung, Il-Won (Dept. of Civil and Environmental Engrg., Sejong Univ.)
Koo, Bo-Young (Namwonkeonseol Engineering Co., Ltd.)
Bae, Deg-Hyo (Dept. of Civil and Environmental Engrg., Sejong Univ.)
Publication Information
Journal of Korea Water Resources Association / v.40, no.9, 2007 , pp. 677-685 More about this Journal
Abstract
The objective of this study is to evaluate the applicability of multi-objective genetic algorithm(MOGA) in order to calibrate the parameters of conceptual rainfall-runoff model, Tank model. NSGA-II, one of the most imitating MOGA implementations, is combined with Tank model and four multi-objective functions such as to minimize volume error, root mean square error (RMSE), high flow RMSE, and low flow RMSE are used. When NSGA-II is employed with more than three multi-objective functions, a number of Pareto-optimal solutions usually becomes too large. Therefore, selecting several preferred Pareto-optimal solutions is essential for stakeholder, and preference-ordering approach is used in this study for the sake of getting the best preferred Pareto-optimal solutions. Sensitivity analysis is performed to examine the effect of initial genetic parameters, which are generation number and Population size, to the performance of NSGA-II for searching the proper paramters for Tank model, and the result suggests that the generation number is 900 and the population size is 1000 for this study.
Keywords
Multi-objective genetic algorithm; Tank model; Preference ordering; Pareto optimal solution; Sensitivity analysis;
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