Daily Discharge Forecasting Based on Support Vector Regression

Li Ying Wang; Wei Guo Zhao

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

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 113-116Daily Discharge Forecasting Based on Support...

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.

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Periodical:

Advanced Materials Research (Volumes 113-116)

Pages:

386-389

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.113-116.386

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Online since:

June 2010

Authors:

Li Ying Wang, Wei Guo Zhao

Keywords:

Artificial Neural Network (ANN), Daily Discharge Forecasting, Support Vector Machine (SVM), Support Vector Regression (SVR)

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