中文摘要：

利用支持向量机（SVM）模型对大磁暴期间Dst指数进行预报研究.以1995—2014年期间的80次大磁暴（Dst≤-100 nT）事件共2662组观测数据为研究对象,以对应时间的太阳风参数为模型输入参数,同时建立了神经网络模型和线性机模型进行对比,并利用交叉验证提高预测结果的可靠性.为比较不同模型的预测效果,选用相关系数（CC）、均方根误差（RMS）、磁暴期间Dst指数最小值预测结果的平均绝对误差以及Dst指数最小值出现时间预测结果的平均绝对误差等统计量作为对比参数.结果显示SVM模型的预测效果最好,其中相关系数为0.89,均方根误差为24.27 nT,所有磁暴事件的最小Dst值预测平均绝对误差为17.35 nT,最小Dst值出现时间的预测平均绝对误差为3.2 h.为进一步检验模型对不同活动水平磁暴预报效果的可能差异,

英文摘要：

In this study the support vector machine is applied to the forecasting of Dst index during intense geomagnetic storms（Dst ≤-100nT） that occurred from 1995 to 2014.We collect 2662 Dst indices and use the corresponding solar wind data as model input.We also build Neural Network and Linear machine as comparison,and improve the reliability of the predicted results by using K-fold cross validation.For comparison,we calculate the Correlation Coefficient（CC）,the RMS errors,the Mean Absolute Error of the minimum Dst（E_m） and the Mean Absolute Error of the time when the minimum Dst occurred（E_t） between the observed Dst data and the predicted one.As a result,we find that SVM shows the best prediction performance for all events：CC is 0.89,RMS is 24.27 nT,E_m is 17.35 nT and E_t is 3.2 hours respectively.For further comparison,the 80 storm events are divided into two groups depending on the minimum value of Dst index.It is shown that the forecasting performance of SVM is better than other models both in the intense（—200 〈Dstmin ≤-100nT）and the super intense geomagnetic storm（Dstmin ≤- 200 nT） groups.