NDLI: Improved Wavelet Modeling Framework for Hydrologic Time Series Forecasting

Content Provider	Springer Nature Link
Author	Sang, Yan Fang
Copyright Year	2013
Abstract	The combination of wavelet analysis with black-box models presently is a prevalent approach to conduct hydrologic time series forecasting, but the results are impacted by wavelet decomposition of series, and uncertainty cannot be evaluated. In this paper, the method for discrete wavelet decomposition of series was developed, and an improved wavelet modeling framework, WMF for short, was proposed for hydrologic time series forecasting. It is to first separate different deterministic components and remove noise in original series by discrete wavelet decomposition; then, forecast the former and quantitatively describe noise’s random characters; at last, add them up and obtain the final forecasting result. Forecasting of deterministic components is to obtain deterministic forecasting results, and noise analysis is to estimate uncertainty. Results of four hydrologic cases indicate the better performance of the proposed WMF compared with those black-box models without series decomposition. Because of having reliable hydrologic basis, showing high effectiveness in accuracy, eligible rate and forecasting period, and being capable of uncertainty evaluation, the proposed WMF can improve the results of hydrologic time series forecasting.
Starting Page	2807
Ending Page	2821
Page Count	15
File Format	PDF
ISSN	09204741
Journal	Water Resources Management
Volume Number	27
Issue Number	8
e-ISSN	15731650
Language	English
Publisher	Springer Netherlands
Publisher Date	2013-03-03
Publisher Institution	European Water Resources Association (EWRA)
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Hydrologic time series forecasting Wavelet Black-box model Noise Statistical analysis Uncertainty Hydrogeology Geotechnical Engineering & Applied Earth Sciences Atmospheric Sciences Civil Engineering Environment
Content Type	Text
Resource Type	Article
Subject	Water Science and Technology Civil and Structural Engineering

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