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- W2186223821 abstract "Neural networks models are applied for the hydrological flood forecasting and disaster prevention system in small catchment, South Korea. Two kinds of neural networks models such as the multilayer perceptron neural networks model (MLP-NNM) and the cascade correlation neural networks model (CCNNM) are used in this study. The optimal parameters are set up during the training performance of the neural networks models. We apply them to evaluate the testing performance of the neural networks models. Neural networks models are used to forecast the flood streamflow at the Musung station (No.1) of the Wistream catchment, which was one of the IHP (International Hydrological Program) representative small catchments in South Korea. Cascade correlation neural networks model shows the better statistical results than those of the multilayer perceptron neural networks model. Using the results, we can construct / develop the flood forecasting and disaster prevention system, which include the structure and datainformation aspects in small catchment. Further-more, neural networks models generally spend less time for the training performance and can be easily used by the hydrologists and engineers with little background knowledge of the neural networks theory." @default.
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- W2186223821 date "2009-01-01" @default.
- W2186223821 modified "2023-09-25" @default.
- W2186223821 title "Neural Networks Models for the Flood Forecasting and Disaster Prevention System in the Small Catchment" @default.
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