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利用支持向量机预测大磁暴期间Dst指数的变化

彭宇翔 吕建永 顾赛菊

彭宇翔, 吕建永, 顾赛菊. 利用支持向量机预测大磁暴期间Dst指数的变化[J]. 空间科学学报, 2016, 36(6): 866-874. doi: 10.11728/cjss2016.06.866
引用本文: 彭宇翔, 吕建永, 顾赛菊. 利用支持向量机预测大磁暴期间Dst指数的变化[J]. 空间科学学报, 2016, 36(6): 866-874. doi: 10.11728/cjss2016.06.866
PENG Yuxiang, LÜ Jianyong, GU Saiju. Application of Support Vector Machine to the Forecasting of Dst Index During Geomagnetic Storm[J]. Journal of Space Science, 2016, 36(6): 866-874. doi: 10.11728/cjss2016.06.866
Citation: PENG Yuxiang, LÜ Jianyong, GU Saiju. Application of Support Vector Machine to the Forecasting of Dst Index During Geomagnetic Storm[J]. Journal of Space Science, 2016, 36(6): 866-874. 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
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

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

  • 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.

     

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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

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