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Wavelet Regression Technique for Streamflow Prediction

Author

Listed:
  • Murat Kucuk
  • Necati Ağirali-super-˙oğlu

Abstract

In order to explain many secret events of natural phenomena, analyzing non-stationary series is generally an attractive issue for various research areas. The wavelet transform technique, which has been widely used last two decades, gives better results than former techniques for the analysis of earth science phenomena and for feature detection of real measurements. In this study, a new technique is offered for streamflow modeling by using the discrete wavelet transform. This new technique depends on the feature detection characteristic of the wavelet transform. The model was applied to two geographical locations with different climates. The results were compared with energy variation and error values of models. The new technique offers a good advantage through a physical interpretation. This technique is applied to streamflow regression models, because they are simple and widely used in practical applications. However, one can apply this technique to other models.

Suggested Citation

  • Murat Kucuk & Necati Ağirali-super-˙oğlu, 2006. "Wavelet Regression Technique for Streamflow Prediction," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(9), pages 943-960.
    • Handle: RePEc:taf:japsta:v:33:y:2006:i:9:p:943-960
    DOI: 10.1080/02664760600744298
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    References listed on IDEAS

    as
    1. Mehmetcik Bayazit & Hafzullah Aksoy, 2001. "Using wavelets for data generation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(2), pages 157-166.
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