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http://dx.doi.org/10.3741/JKWRA.2012.45.12.1321

Forecast of the Daily Inflow with Artificial Neural Network using Wavelet Transform at Chungju Dam  

Ryu, Yongjun (School of Civil and Environmental Engineering, Yonsei Univ.)
Shin, Ju-Young (School of Civil and Environmental Engineering, Yonsei Univ.)
Nam, Woosung (School of Civil and Environmental Engineering, Yonsei Univ.)
Heo, Jun-Haeng (School of Civil and Environmental Engineering, Yonsei Univ.)
Publication Information
Journal of Korea Water Resources Association / v.45, no.12, 2012 , pp. 1321-1330 More about this Journal
Abstract
In this study, the daily inflow at the basin of Chungju dam is predicted using wavelet-artificial neural network for nonlinear model. Time series generally consists of a linear combination of trend, periodicity and stochastic component. However, when framing time series model through these data, trend and periodicity component have to be removed. Wavelet transform which is denoising technique is applied to remove nonlinear dynamic noise such as trend and periodicity included in hydrometeorological data and simple noise that arises in the measurement process. The wavelet-artificial neural network (WANN) using data applied wavelet transform as input variable and the artificial neural network (ANN) using only raw data are compared. As a results, coefficient of determination and the slope through linear regression show that WANN is higher than ANN by 0.031 and 0.0115 respectively. And RMSE and RRMSE of WANN are smaller than those of ANN by 37.388 and 0.099 respectively. Therefore, WANN model applied in this study shows more accurate results than ANN and application of denoising technique through wavelet transforms is expected that more accurate predictions than the use of raw data with noise.
Keywords
wavelet transform; denoising; artificial neural network;
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Times Cited By KSCI : 1  (Citation Analysis)
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