Application Research of Support Vector Machine Based on Particle Swarm Optimization in Runoff Forecasting

被引：2

作者：

Wang, Lixue ^{[1
]}

Wang, Lina ^{[2
]}

Li, Guofeng ^{[3
]}

Luan, Ce ^{[1
]}

Sun, Feifei ^{[1
]}

机构：

[1] Shenyang Agr Univ, Coll Water Conservancy, Shenyang 110866, Peoples R China

[2] Territorial Resources Explorat Planning Inst Co L, Qiqihar 161006, Peoples R China

[3] Dahuofang Reservoir Water Supply Engn Construct, Shenyang 110166, Liaoning Provin, Peoples R China

来源：

VIBRATION, STRUCTURAL ENGINEERING AND MEASUREMENT II, PTS 1-3 | 2012年 / 226-228卷

关键词：

Support Vector Machine; Particle Swarm Optimization; Parameter Optimization; Runoff Forecast; Dahuofang Reservoir;

D O I：

10.4028/www.scientific.net/AMM.226-228.2303

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

In view of the little sample, less data problems, mid-and-long term hydrologic forecasting is a case of which, Support Vector Machine (SVM) can solve this kind of problems perfectly. This paper introduced the basic optimization procedure and PSO-SVM modeling procedure. The PSO-SVM model has been applied in forecasting the monthly runoff of Dahuofang reservoir. The comparison between PSO-SVM and not-optimized SVM implied that the PSO-SVM has a fast convergence speed and strong generalization capability, also the related error has been decreased from 15.5% to 11.9%.

引用

页码：2303 / +

页数：2

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