利用支持向量机预测大磁暴期间<i>Dst</i>指数的变化

彭宇翔; 吕建永; 顾赛菊

doi:10.11728/cjss2016.06.866

利用支持向量机预测大磁暴期间Dst指数的变化

doi: 10.11728/cjss2016.06.866

南京信息工程大学数学与统计学院南京 210044

基金项目:

国家自然科学基金项目（U1631107，41574158）

详细信息

通讯作者:
吕建永,E-mail:jianyong_lyu@163.com

中图分类号: P353
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- 被引次数: 0
出版历程
- 收稿日期: 2015-10-10
- 修回日期: 2016-04-01
- 刊出日期: 2016-11-15

Application of Support Vector Machine to the Forecasting of Dst Index During Geomagnetic Storm

School of Mathematics and Statistics, Nanjing University of Information Science andTechnology, Nanjing 210044

摘要

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≤-100nT）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24.27nT，所有磁暴事件的最小Dst值预测平均绝对误差为17.35nT，最小Dst值出现时间的预测平均绝对误差为3.2h.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，将所有磁暴事件分为大磁暴（-200 <Dst ≤-100nT）和特大磁暴（Dst≤-200nT）两组进行预测，发现两组事件的预测效果依然是SVM模型最好.
- 支持向量机 /
- 预测 /
- 地磁暴 /
- Dst指数
Abstract: 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 24.27nT, E_m is 17.35nT 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 <Dst_min ≤-100nT) and the super intense geomagnetic storm (Dst_min ≤-200nT) groups.
- Support Vector Machine (SVM) /
- Forecasting /
- Geomagnetic storm /
- Dst index

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参考文献(22)

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