NDLI: A study of the hydrological prediction model based on wavelet de-noise method

Content Provider	IEEE Xplore Digital Library
Author	Geng Qu Fengyang Min Xiaohu Guo Yonghui Zhu
Copyright Year	2010
Description	Author affiliation: State key Laboratory of water Resources and Hydropower Engineering Science, Wuhan, China (Geng Qu) \|\| Changjiang River Scientific Research Institute, Wuhan, China (Fengyang Min; Xiaohu Guo; Yonghui Zhu)
Abstract	The general hydrological time series data inevitably contained “noise” for various factors. The existence of “noise” changed the autocorrelation and covered the real change characteristics of the hydrological time series. We based this study on the stochastic model of wavelet de-noise and applied the model to the hydrological prediction. In this paper five decades runoff data of Yichang Station (1952–2002, monthly) were analyzed and were de-noised by the wavelet method. A stochastic model is established on the basis of the variable feature of de-noised series data and the hydrological time series data were predicted by the model. Results of the study indicate that the wavelet analysis has great priority in de-noise procession and trend prediction. The predicted model on the basis of the de-noise time-series has a high precision for the hydrological data prediction.
Starting Page	3763
Ending Page	3767
File Size	279287
Page Count	5
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5583185
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms prediction model Time series analysis Noise Stochastic processes Predictive models wavelet Wavelet analysis de-noise Data models hydrological time-series
Content Type	Text
Resource Type	Article

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