Application Study of Least Squares Support Vector Machines in Streamflow Forecast

被引：0
|
作者：
Zhao, Yan [1 ]
Dong, Zengchuan [1 ]
Li, Qinghang [2 ]
机构：
[1] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Jiangsu, Peoples R China
[2] Changjiang Inst Survey Planning Design & Res, Wuhan 430010, Hunan, Peoples R China
来源：
ADVANCES IN HYDROLOGY AND HYDRAULIC ENGINEERING, PTS 1 AND 2 | 2012年 / 212-213卷
关键词：
LS-SVM; streamflow prediction; kernel function; regression analysis;
D O I：
10.4028/www.scientific.net/AMM.212-213.436
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
In this article, the Least Square Support Vector Machine(LS-SVM) regression analysis and prediction methods were briefly introduced. Radial basis kernel function was chosen and a streamflow forecast model based on the toolbox of Matlab was constructed. Then the model was validated with a case study. After the model validation with a case study, it could be seen that the prediction model shows high accuracy and convergence speed. According to the analysis of the results, it is feasible for LS-SVM utilization in streamflow forecast.
引用
收藏
页码：436 / +
页数：2
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