RBF Prediction Model Based on EMD for Forecasting GPS Precipitable Water Vapor and Annual Precipitation

摘要：

　　The forecast of precipitations is important in meteorology and atmospheric sciences.A new model is proposed based on empirical mode decomposition and the RBF neural network.Firstly,GPS PWV time series is broken down into series of different scales intrinsic mode function.Secondly,the phase-space reconstruction is done.Thirdly,each component is predicted by RBF.Finally,the final prediction value is reconstructed.Next,the model is tested on annual precipitation sequence from 2001 to 2010 in northeast China.The result shows that predictive value is close to the actual precipitation,which can better reflect the actual precipitation change.From 2001 to 2010,the maximum deviation of the predicted values never exceeds 4%.The testing results show that the proposed model can increase precipitation forecasting accuracies not only in GPS PWV but also in annual precipitation.

关键词： Empirical Mode Decomposition RBF Neural Network GPS Precipitable Water Vapor Precipitation

作者: Yanping LiU Yong Wang Zhen Wang

作者单位: School of Civil Engineering, Central South University, Changsha 410075, China Hebei Researching Center of Earthquake Engineering, Tangshan 063009, China Department of Scientific Research, Hebei United University, Tangshan 063009

会议类型: 国际会议

会议名称: 2013 2nd International Conference on Systems Engineering and Modeling(ICSEM-13)(2013年第二届系统工程与建模国际会议)

会议地点: 北京

会议语种:英文

页码: 51-55

在线出版日期: 2013-04-19（万方平台首次上网日期，不代表论文的发表时间）