Paper Title:
Daily Discharge Forecasting Based on Support Vector Regression
  Abstract

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, 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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