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Improved Wavelet Modeling Framework for Hydrologic Time Series Forecasting

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  • Yan-Fang Sang

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. Copyright Springer Science+Business Media Dordrecht 2013

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  • Yan-Fang Sang, 2013. "Improved Wavelet Modeling Framework for Hydrologic Time Series Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2807-2821, June.
    • Handle: RePEc:spr:waterr:v:27:y:2013:i:8:p:2807-2821
    DOI: 10.1007/s11269-013-0316-1
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    1. Yan-Fang Sang, 2012. "A Practical Guide to Discrete Wavelet Decomposition of Hydrologic Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(11), pages 3345-3365, September.
    2. Subimal Ghosh & Sudhir Katkar, 2012. "Modeling Uncertainty Resulting from Multiple Downscaling Methods in Assessing Hydrological Impacts of Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(12), pages 3559-3579, September.
    3. Dushmanta Dutta & Wendy Welsh & Jai Vaze & Shaun Kim & David Nicholls, 2012. "A Comparative Evaluation of Short-Term Streamflow Forecasting Using Time Series Analysis and Rainfall-Runoff Models in eWater Source," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4397-4415, December.
    4. Changsam Jeong & Ju-Young Shin & Taesoon Kim & Jun-Haneg Heo, 2012. "Monthly Precipitation Forecasting with a Neuro-Fuzzy Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4467-4483, December.
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