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Improving Hydrological Process Modeling Using Optimized Threshold-Based Wavelet De-Noising Technique

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  • Peyman Abbaszadeh

    (Portland State University)

Abstract

The extent of the noise on hydrological data is inevitable, which reduces the efficiency of Data-Driven Models (DDMs). Despite of this fact that the DDMs such as Artificial Neural Network (ANN) are capable of nonlinear functional mapping between a set of input and output variables, but refining of the time series through data pre-processing methods can provide with the possibility to increase the performance of these set of models. The main objective of this study is to propose a new method called Optimized Threshold-Based Wavelet De-noising technique (OTWD) to de-noise hydrological time series and improve the prediction accuracy while the DDM is being used. For this purpose, in the first step, Wavelet-ANN (WNN) model was developed for identifying suitable wavelet function and maximum decomposition level. Afterward, sub-signals of original precipitation time series which were determined in the first step were de-noised by using of OTWD technique. Therefore, these clean sub-signals of precipitation time series were imposed as input data to the ANN to predict the precipitation one time step ahead. The results showed that OTWD technique could improve the efficiency of WNN model dramatically; this outcome was reported by the different efficiency criterions such as Nash-Sutcliffe Efficiency (NSE = 0.92), Root Mean Squared Error (RMSE = 0.0103), coefficient correlation of linear regression (R = 0.93), Peak Value Criterion (PVC = 0.021) and Low Value Criterion (LVC = 0.026). The best fitted WNN model in comparison by proposed model showed weaker performance by the NSE, RMSE, R, PVC and LVC values of 0.86, 0.043, 0.87, 0.034 and 0.045, respectively.

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  • 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.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:5:d:10.1007_s11269-016-1246-5
    DOI: 10.1007/s11269-016-1246-5
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    References listed on IDEAS

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    1. Sanjeet Kumar & Mukesh Tiwari & Chandranath Chatterjee & Ashok Mishra, 2015. "Reservoir Inflow Forecasting Using Ensemble Models Based on Neural Networks, Wavelet Analysis and Bootstrap Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4863-4883, October.
    2. 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.
    3. Youngmin Seo & Sungwon Kim & Vijay Singh, 2015. "Estimating Spatial Precipitation Using Regression Kriging and Artificial Neural Network Residual Kriging (RKNNRK) Hybrid Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2189-2204, May.
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    Cited by:

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    3. Weide Li & Demeng Kong & Jinran Wu, 2017. "A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting," Energies, MDPI, vol. 10(5), pages 1-16, May.

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