Proceedings of the Korea Water Resources Association Conference (한국수자원학회:학술대회논문집)
- 2001.05a
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- Pages.45-51
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- 2001
Forecasting of Daily Inflows Based on Regressive Neural Networks
- Shin, Hyun-Suk (Department of Civil Engineering, Pusan University) ;
- Kim, Tae-Woong (Department of Civil Engineering, University of Arizona) ;
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Kim, Joong-Hoon
(Department of Civil and Environmental Engineering, Korea University)
- Published : 2001.05.01
Abstract
The daily inflow is apparently one of nonlinear and complicated phenomena. The nonlinear and complexity make it difficult to model the prediction of daily flow, but attractive to try the neural networks approach which contains inherently nonlinear schemes. The study focuses on developing the forecasting models of daily inflows to a large dam site using neural networks. In order to reduce the error caused by high or low outliers, the back propagation algorithm which is one of neural network structures is modified by combining a regression algorithm. The study indicates that continuous forecasting of a reservoir inflow in real time is possible through the use of modified neural network models. The positive effect of the modification using tole regression scheme in BP algorithm is showed in the low and high ends of inflows.
Keywords