Daily Discharge Forecasting based on Support Vector Regression

摘要：

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.

关键词： support vector regression SVM artificial neural network daily discharge forecasting

作者: Wang Li-ying Zhao Wei-guo

作者单位: College of Water Conservancy and Hydropower,Hebei University of Engineering Han Dan,056038,China

会议类型: 国际会议

会议名称: 2010 International Conference of Environment Materials and Environment Management(2010年环境材料与环境管理国际学术会议 EMEM 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 386-389

在线出版日期: 2010-07-24（万方平台首次上网日期，不代表论文的发表时间）