Support Vector Regression Based on Particle Swarm Optimization for Rainfall Forecasting

摘要：

This study applies a novel neural network technique, support vector regression (SVR), to rainfall forecasting. To build an effective SVR model, SVRs parameters must be set carefully. This study proposes a novel approach, known as particle swarm optimization algorithm (SVR-PSO), which searches for SVRs optimal parameters, and then adopts the optimal parameters to construct the SVR models. The monthly rainfall in the Guangxi of China during 1954-2008 were employed as the data set. The experimental results demonstrate that SVR-PSO outperforms the SVR models based on the normalized mean square error (NMSE) and mean absolute percentage error (MAPE).

关键词： Support Vector Regression particle swarm optimization Rainfall Forecasting

作者: Shian Zhao Lingzhi Wang

作者单位: Department of Mathematics and Computer Science Baise University Baise,Guangxi,China Department of Mathematics and Computer Science Liuzhou Teachers College Liuzhou,Guangxi,China

会议类型: 国际会议

会议名称: The Third International Joint Conference on Computational Science and Optimization(第三届计算科学与优化国际大会 CSO 2010)

会议地点: 黄山

会议语种:英文

页码: 484-487

在线出版日期: 2010-05-28（万方平台首次上网日期，不代表论文的发表时间）