Daily Discharge Forecasting Based on Support Vector Regression

Abstract:

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In this paper, we apply support vector regression (SVR) for daily discharge forecasting and compare its results to other prediction methods using real daily discharge data. Since support vector machines have greater generalization ability and guarantee global minima for given training data, it is believed that support vector regression will perform well for time series analysis. Compared to other predictors, our results show that the SVR predictor can reduce significantly both relative mean errors and root mean squared errors of predicted daily discharge.

Info:

Periodical:

Advanced Materials Research (Volumes 113-116)

Edited by:

Zhenyu Du and X.B Sun

Pages:

386-389

DOI:

10.4028/www.scientific.net/AMR.113-116.386

Citation:

L. Y. Wang and W. G. Zhao, "Daily Discharge Forecasting Based on Support Vector Regression", Advanced Materials Research, Vols. 113-116, pp. 386-389, 2010

Online since:

June 2010

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Price:

$35.00

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