| Abstract |
| In order to resolve the rainfall-runoff forecast model s uncertainty of model parameters and to increase the model s output, the study utilized Neural Networks model such as ANN and GRNN model. which mathematically interpret human thought processes. In order to calibrate and verify the runoff forecast model, seven flood events(1997-2002) observed at the Kumho water level gage station located on the midstream of Kumho river were chosen, of which five were used as calibration data and two as that for verification. First, as a result of applying ANN model, of the ten models, the ANN-8-8(10 input layer nodes, 8 nodes in the first hidden layer, 8 nodes in the second hidden layer, 1 output layer node) model was found to be the more suitable model in an actual hydro-event. Second, as a result of applying GRNN model, of the nine models, the GN-4a(l0 input layer nodes, 4 nodes in the pattern layer, spread σ=0.05, 1 output layer node) model was considered suitable. In addition, the numbers of pattern layer node increased, but it didn`t increase the learning rate. Finally, according to the results of the statistical analysis of the ANN and GRNN model, it was shown that ANN model was better. However, since there are numerous difficulties in determining the superiority of a model based on data only from the seven heavy rainfall events used in this study, it is judged that continued application and study based on more quantitative and qualitative data are necessary. |
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| Key Words |
| Artificial Neural Network, ANN model, Generalized Regression Neural Network, GRNN model |
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