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- W2216197181 abstract "Uncertainty analysis is used to eliminate the climatic variables of input nodes and construct the model of an optimal type from COMBINE-GRNNMGA (Type-1), which has been developed from Kim and Kim (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 ETr, 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." @default.
- W2216197181 created "2016-06-24" @default.
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- W2216197181 date "2007-10-01" @default.
- W2216197181 modified "2023-09-23" @default.
- W2216197181 title "Derivation of the Optimal Artificial Neural Networks Model using Uncertainty Analysis" @default.
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