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A Threshold Based Wavelet Denoising Method for Hydrological Data Modelling

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  • Chien-ming Chou

Abstract

This work developed a novel framework for considering wavelet denoising in linear perturbation models (LPMs) and simple linear models (SLMs). Rainfall and runoff time series data were decomposed using wavelet transforms to acquire approximate and detailed rainfall and runoff signals, respectively, at various resolution levels. At each resolution level, threshold quantifications were performed by setting the values of a detailed signal below a certain threshold to zero. The denoised rainfall and runoff time series data were obtained from the approximation at the final resolution level and processed detailed signals using threshold quantification at all resolution levels of rainfall and runoff, respectively, by wavelet reconstruction. The data were then applied to the SLM and regarded as the smooth seasonal mean employed in the LPM. The noise, i.e., original time series value minus denoised time series value, was employed as the perturbation term in the LPM. Moreover, a linear relationship between input and output noise was assumed. The denoised runoff and estimated noise of runoff were summed to estimate overall runoff in the LPM. To verify the accuracy of the proposed method, daily rainfall–runoff data were analyzed for an upstream area of the Kee-Lung River. The analytical results demonstrate that wavelet denoising enhances rainfall–runoff modelling precision for the LPM. Copyright Springer Science+Business Media B.V. 2011

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  • Chien-ming Chou, 2011. "A Threshold Based Wavelet Denoising Method for Hydrological Data Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(7), pages 1809-1830, May.
    • Handle: RePEc:spr:waterr:v:25:y:2011:i:7:p:1809-1830
    DOI: 10.1007/s11269-011-9776-3
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    Citations

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    Cited by:

    1. Quoc Bao Pham & Tao-Chang Yang & Chen-Min Kuo & Hung-Wei Tseng & Pao-Shan Yu, 2021. "Coupling Singular Spectrum Analysis with Least Square Support Vector Machine to Improve Accuracy of SPI Drought Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 847-868, February.
    2. By Huang & Hund-Der Yeh, 2012. "Parameter Identification for a Slug Test in a Well with Finite-Thickness Skin Using Extended Kalman Filter," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(14), pages 4039-4057, November.
    3. Salvatore Campisi-Pinto & Jan Adamowski & Gideon Oron, 2012. "Forecasting Urban Water Demand Via Wavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(12), pages 3539-3558, September.
    4. 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.
    5. Peyman Abbaszadeh, 2016. "Improving Hydrological Process Modeling Using Optimized Threshold-Based Wavelet De-Noising Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1701-1721, March.
    6. Yan-Fang Sang & Zhonggen Wang & Changming Liu, 2015. "Wavelet Neural Modeling for Hydrologic Time Series Forecasting with Uncertainty Evaluation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1789-1801, April.
    7. Vahid Moosavi & Mehdi Vafakhah & Bagher Shirmohammadi & Negin Behnia, 2013. "A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1301-1321, March.
    8. R. Venkata Ramana & B. Krishna & S. Kumar & N. Pandey, 2013. "Monthly Rainfall Prediction Using Wavelet Neural Network Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3697-3711, August.
    9. Peyman Abbaszadeh, 2016. "Improving Hydrological Process Modeling Using Optimized Threshold-Based Wavelet De-Noising Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1701-1721, March.
    10. Sajjad Abdollahi & Jalil Raeisi & Mohammadreza Khalilianpour & Farshad Ahmadi & Ozgur Kisi, 2017. "Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4855-4874, December.

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