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http://dx.doi.org/10.3741/JKWRA.2007.40.1.089

Neural Networks-Genetic Algorithm Model for Modeling of Nonlinear Evaporation and Evapotranpiration Time Series. 2. Optimal Model Construction by Uncertainty Analysis  

Kim, Sung-Won (Dept. of Rail. and Civil Engr., Dongyang University)
Kim, Hung-Soo (School of Civil and Environ. Engr., Inha University)
Publication Information
Journal of Korea Water Resources Association / v.40, no.1, 2007 , pp. 89-99 More about this Journal
Abstract
Uncertainty analysis is used to eliminate the climatic variables of input nodes and construct the model of an optimal type from COMBINE-GRNNM-GA(Type-1), which have been developed in this issue(2007). The input variable which has the lowest smoothing factor during the training performance, is eliminated from the original COMBINE-GRNNM-GA (Type-1). And, the modified COMBINE-GRNNM-GA(Type-1) is retrained to find the new and lowest smoothing factor of the each climatic variable. The input variable which has the lowest smoothing factor, implies the least useful climatic variable for the model output. Furthermore, The sensitive and insensitive climatic variables are chosen from the uncertainty analysis of the input nodes. The optimal COMBINE-GRNNM-GA(Type-1) is developed to estimate and calculate the PE which is missed or ungaged and the $ET_r$ which is not measured with the least cost and endeavor Finally, the PE and $ET_r$. maps can be constructed to give the reference data for drought and irrigation and drainage networks system analysis using the optimal COMBINE-GRNNM-GA(Type-1) in South Korea.
Keywords
Uncertainty analysis; Regression analysis; Smoothing factor; Map construction;
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Times Cited By KSCI : 2  (Citation Analysis)
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