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Title:
Nonlinear Prediction Model for Hydrologic Time Series Based on Wavelet Decomposition
Authors:
Kwon, H.; Khalil, A.; Brown, C.; Lall, U.; Ahn, H.; Moon, Y.
Affiliation:
AA(Earth and Environmental Engineering, Columbia University, 918 Mudd, HKSM 500 W 120th Street, New York, NY 10027 United States ), AB(Earth and Environmental Engineering, Columbia University, 918 Mudd, HKSM 500 W 120th Street, New York, NY 10027 United States ), AC(International Research Institute for Climate Prediction, 133 Monell Bldg, Palisades, NY 10964 United States ), AD(Earth and Environmental Engineering, Columbia University, 918 Mudd, HKSM 500 W 120th Street, New York, NY 10027 United States ; International Research Institute for Climate Prediction, 133 Monell Bldg, Palisades, NY 10964 United States ), AE(Research Hydrologist, NPS - South Florida Ecosystem Office, 950 N. Krome Ave., Homestead, FL 33030 United States ), AF(Department of Civil Engineering, University of Seoul, Jeonnong-dong, Dongdaemun-gu, Seoul, 130-743 Korea, Republic of )
Publication:
American Geophysical Union, Fall Meeting 2005, abstract #H13G-1392
Publication Date:
12/2005
Origin:
AGU
Keywords:
1807 Climate impacts, 1808 Dams, 1816 Estimation and forecasting, 1821 Floods, 1860 Streamflow
Bibliographic Code:
2005AGUFM.H13G1392K

Abstract

Traditionally forecasting and characterizations of hydrologic systems is performed utilizing many techniques. Stochastic linear methods such as AR and ARIMA and nonlinear ones such as statistical learning theory based tools have been extensively used. The common difficulty to all methods is the determination of sufficient and necessary information and predictors for a successful prediction. Relationships between hydrologic variables are often highly nonlinear and interrelated across the temporal scale. A new hybrid approach is proposed for the simulation of hydrologic time series combining both the wavelet transform and the nonlinear model. The present model employs some merits of wavelet transform and nonlinear time series model. The Wavelet Transform is adopted to decompose a hydrologic nonlinear process into a set of mono-component signals, which are simulated by nonlinear model. The hybrid methodology is formulated in a manner to improve the accuracy of a long term forecasting. The proposed hybrid model yields much better results in terms of capturing and reproducing the time-frequency properties of the system at hand. Prediction results are promising when compared to traditional univariate time series models. An application of the plausibility of the proposed methodology is provided and the results conclude that wavelet based time series model can be utilized for simulating and forecasting of hydrologic variable reasonably well. This will ultimately serve the purpose of integrated water resources planning and management.
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