Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Forecast of the Daily Inflow with Artificial Neural Network using Wavelet Transform at Chungju Dam

웨이블렛 변환을 적용한 인공신경망에 의한 충주댐 일유입량 예측

  • 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.)
  • 류용준 (연세대학교 대학원 토목공학과) ;
  • 신주영 (연세대학교 대학원 토목공학과) ;
  • 남우성 (연세대학교 대학원 토목공학과) ;
  • 허준행 (연세대학교 사회환경시스템공학부 토목환경공학과)
  • Received : 2012.06.29
  • Accepted : 2012.09.27
  • Published : 2012.12.31
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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.

본 연구에서는 비선형적 모델인 웨이블렛-인공신경망을 적용하여 충주댐 유역의 일유입량을 예측하였다. 일반적으로 시계열 자료는 경향성, 주기성 및 추계학적 성분의 선형조합으로 이루어져 있다. 그러나 이러한 자료를 통해 시계열 모형 구축 시 경향성 및 주기성은 제거되어야하는 성분이다. 따라서 수문기상자료에 포함되어있는 경향성 및 주기성과 같은 비선형 동역학적 잡음과 측정과정에서 발생하는 단순잡음을 제거시키기 위해 디노이징기법인 웨이블렛 변환을 적용하였다. 웨이블렛 변환을 적용한 자료를 입력자료로 사용한 웨이블렛-인공신경망(WANN)과 원자료를 사용한 인공신경망(ANN)을비교하였다. 산정결과 결정계수와 선형회귀를 통한 기울기는 WANN이 ANN보다 각각0.032, 0.0115 더 큰값을 나타냈고, 타겟값과 예측값 사이의 오차를 나타내는 RMSE와 RRMSE는 WANN 모형이 ANN 보다 각각 37.388, 0.099 더 작은값을 나타냈다. 따라서 본 연구에서 적용한 WANN 모형이 ANN 보다 정확한 결과를 나타내었으며, 웨이블렛 변환을 통한 디노이징 기법의 적용이 잡음이 포함되어 있는 원자료의 사용보다 더 정확한 예측을 하는 것으로 판단된다.

Keywords

References

  1. Baek, U.J., Kang, S.J., and Chung, C.B. (1998). "Comparison of Various Denoising Algorithms Using Wavelet Transformation." Theories and Applications of Chemical Engineering, Vol. 4, No. 2, pp. 2313-2316.
  2. Bishop, C.M. (1995). "Neural networks for pattern recognition." Oxford. pp. 140-148.
  3. Cannas, B., Fanni, A., See, L., and Sias, G. (2006). "Data preprocessing for river flow forecasting using neural networks: wavelet transforms and data partitioning." Physics and Chemistry of the Earth, Vol. 31, No. 18, pp. 1164-1171. https://doi.org/10.1016/j.pce.2006.03.020
  4. Cho, Y.J., and Kim, J.M. (1998). "Water Supply forecast Using Multiple ARMA Model Based on the Analysis of Water Consumption Mode with Wavelet Transform." Journal of Korea Water Resources Association, Vol. 31, No. 3, pp. 317-326.
  5. Daniel, B.G., Francisco, J.D.P., David, G.O., Jose, F.D.H., Miriam, A.-R., Mario, M.Z., and Isabel, T.D. (2010). "Wavelet-Based denoising for traffic volume time series forecasting with self-organizing neural networks." Computer-Aided Civil and Infrastructure Engineering, Vol. 25, No. 7, pp. 530-545. https://doi.org/10.1111/j.1467-8667.2010.00668.x
  6. Daubechies, I. (1988). "Orthonormal bases of compactly supported wavelets." Communications on Pure and Applied Mathematics, Vol. 41, No. 7, pp. 909-996. https://doi.org/10.1002/cpa.3160410705
  7. Daubechies, I. (1990). "The wavelet transform, timefrequency localization and signal analysis." IEEE Transactions on Information Theory, Vol. 36, No. 5, pp. 961-1005. https://doi.org/10.1109/18.57199
  8. Donoho, D.L., and Johnstone, I.M. (1994). "Ideal spatial adaptation by wavelet shrinkage." Biometrika, Vol. 81, No. 3, pp. 425-455. https://doi.org/10.1093/biomet/81.3.425
  9. Donoho, D.L., and Johnstone, I.M. (1995). "Adapting to unknown smoothness via wavelet shrinkage." Journal of the Americal Statistical Associiation, Vol. 90, No. 432, pp. 1200-1224. https://doi.org/10.1080/01621459.1995.10476626
  10. Farge, M. (1992). "Wavelet transforms and their applications to turbulence." Annual Reviewof Fluid Mechanics, Vol. 24, pp. 395-457. https://doi.org/10.1146/annurev.fl.24.010192.002143
  11. Gao, J., Sultan, H., Hu, J., and Tung, W.W. (2010). "Denoising nonlinear time series by adaptive filtering and wavelet shrinkage: A comparison." IEEE Signal Process Letter, Vol. 17, No. 3, pp. 237-240. https://doi.org/10.1109/LSP.2009.2037773
  12. Graps, A. (1995). "An introduction to wavelets." IEEE Computational Science & Engineering, Vol. 2, No.2, pp. 50-61. https://doi.org/10.1109/99.388960
  13. Ha, J.C., and Lee, D.H. (2010). "Working note for marketing analysis." Adambooks, p. 84.
  14. Hsu, K., Gupta, H.V., and Sorooshian, S. (1995). "Artificial neural network modeling of rainfall-runoff process." Water Resources Research, Vol. 31, No. 10, p. 2517.
  15. Jin, Y.H., Park, S.C., and Lee, Y.K. (2005). "Application of Wavelet Transform for Extraction of long-and Short-term Components in a Hydrological Time Series." KSCE Journal of Civil Engineering, Vol. 25, No. 6B, pp. 493-499.
  16. Kim, T.W., and Valdes, J.B. (2003). "Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks." Journal ofHydrologic Engineering, Vol. 8, No. 6, pp. 319-328. https://doi.org/10.1061/(ASCE)1084-0699(2003)8:6(319)
  17. Kozlowski, B. (2005). "Time series denoising with wavelet transform." Journal of Telecomunications and Information Technology, Vol. 3, pp. 91-95.
  18. Lee, N.Y., and Kim, Y.Y. (1999). "What is the Wavelet?" Korean Society for Noise and Vibration Engineering (KSNVE), Vol. 9, No. 5, pp. 867-875.
  19. Smith, L.C., Turcotte, D.L., and Isacks, B.L. (1998). "Stream flow characterization and feature detection using a discrete wavelet transform." Hydrological Processes, Vol. 12, No. 2, pp. 233-249. https://doi.org/10.1002/(SICI)1099-1085(199802)12:2<233::AID-HYP573>3.0.CO;2-3
  20. Torrence, C., and Compo, G.P. (1998). "A practical guide to wavelet analysis." Bulletin ofAmerican Meteorological Society, Vol. 79, No. 1, pp. 61-78. https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2