基于支持向量机方法的地球同步轨道相对论电子事件预报模型研究

提出了一种基于支持向量机方法(SVM)的地球同步轨道相对论电子事件预报模型.模型以平均影响值(MIV)作为指标,筛选出预报输入参量.这些参量包括,前一日的大于2MeV电子日积分通量、太阳风速度、太阳风密度、Dst指数和前二日的AE指数.模型包含回归和分类两个部分,可以分别对未来一天的电子日积分通量和相对论电子事件强度的级别做出预报.对2008年样本进行测试,在相对论电子通量的预报中,预报值和实测值之间的线性相关系数为0.85,预报效率为0.71;对相对论电子事件级别预报的准确率为82%,可以较准确区分开事件状态与非事件状态.结果表明,SVM预报模型对相对论电子事件有较好的预报效果.

Study on the Forecasting Method of Relativistic Electron Flux at Geostationary Orbit Based on Support Vector Machine

In this paper the Support Vector Machine(Classification/Regression) is applied to predict the relativistic electron flux at geostationary orbit.The parameters of model are chosen by Mean Impact Values(MIV),including the electron flux,solar wind speed,solar wind density,Dst index on the previous day and AE index during the preceding two days.This model forecasts the level of relativistic electron flux event and the magnitude of electron flux on the coming day.Based on the comparison with original data in 2008,this model can normally categorize active and quite intervals.For predicting the magnitude of relativistic electron flux,the linear correlation coefficient and prediction efficiency is 0.85 and 0.71;and the model can correctly predict the level of energetic electron enhancement event at most of the time(82%).Our result demonstrates this forecasting technique based on SVM is viable and maybe applicable to other subjects.