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基于集成学习的太阳质子事件短期预报方法

宫哲 邹自明 陆阳

宫哲, 邹自明, 陆阳. 基于集成学习的太阳质子事件短期预报方法[J]. 空间科学学报, 2022, 42(3): 340-345. doi: 10.11728/cjss2022.03.210310025
引用本文: 宫哲, 邹自明, 陆阳. 基于集成学习的太阳质子事件短期预报方法[J]. 空间科学学报, 2022, 42(3): 340-345. doi: 10.11728/cjss2022.03.210310025
GONG Zhe, ZOU Ziming, LU Yang. Solar Proton Events Short-time Forecasting Based on Ensemble Learning (in Chinese). Chinese Journal of Space Science, 2022, 42(3): 340-345. DOI: 10.11728/cjss2022.03.210310025
Citation: GONG Zhe, ZOU Ziming, LU Yang. Solar Proton Events Short-time Forecasting Based on Ensemble Learning (in Chinese). Chinese Journal of Space Science, 2022, 42(3): 340-345. DOI: 10.11728/cjss2022.03.210310025

基于集成学习的太阳质子事件短期预报方法

doi: 10.11728/cjss2022.03.210310025
基金项目: 中国科学院“十三五”信息化建设专项资助(XXH13505-04)
详细信息
    作者简介:

    宫哲:E-mail:PhilGong@126.com

  • 中图分类号: P353

Solar Proton Events Short-time Forecasting Based on Ensemble Learning

  • 摘要: 太阳质子事件是一种由太阳活动爆发时喷射并传播到近地空间的高能粒子引起的空间天气现象。这些高能粒子会对航天器和宇航员产生严重危害,对太阳质子事件进行准确的短期预报是航天活动灾害预防的重要内容。针对当前主要预报模型中普遍存在的高虚报率问题,提出了一种基于集成学习的太阳质子事件短期预报方法,利用第23个太阳活动周数据,建立了一种集成8种机器学习模型的太阳质子事件短期预报系统。实验结果表明,本文方法在取得了80.95%的报准率的同时,将虚报率降低至19.05%,相比现有的预报系统具有较为明显的优势。

     

  • 表  1  本文使用的8种机器学习模型

    Table  1.   Machine learning models applied in this study

    模型名称简称引用文献
    Logistic Regression LR Ref. [18]
    Decision Tree DT Ref. [19]
    Random Forest RF Ref. [20]
    Adaptive Boosting AdaBoost Ref. [21]
    Gradient Boosting GBDT Ref. [22]
    Extreme Gradient Boosting XGBoost Ref. [23]
    K-Nearest Neighbour KNN Ref. [24]
    Gaussian Naïve Bayes GNB Ref. [25]
    下载: 导出CSV

    表  2  实验数据的划分方式

    Table  2.   Dataset division strategy

    子数据集数量作用
    训练集 交叉训练集 400条
    训练集的4/5
    交叉验证中训练
    模型
    训练集成
    模型
    交叉验证集 100条
    训练集的1/5
    交叉验证中测试
    模型能力
    测试集 164条 测试集成模型的能力
    下载: 导出CSV

    表  3  八种模型各自在交叉验证中的表现

    Table  3.   Performance of 8 individual models on cross-validation dataset

    模型报准率虚报率F1
    LR0.53250.36020.5753
    DT0.6190.38100.6190
    RF0.57140.14290.6857
    Ada0.52380.21430.6286
    GBDT0.66670.17650.7368
    XGB0.76190.33330.7111
    GNB1.00000.67190.4941
    KNN0.57140.33330.6154
    下载: 导出CSV

    表  4  集成模型对测试集中的太阳质子事件的预报结果

    Table  4.   SPEs prediction result of the ensemble model on the test set

    质子事件
    发生时间
    峰值
    通量
    是否
    漏报
    质子事件
    发生时间
    峰值
    通量
    是否
    漏报
    1998-04-2017002002-08-1426
    1998-11-07112002-08-24317
    1999-06-04642003-10-26466
    2000-11-24942003-11-0230
    2001-04-0211102003-12-0289
    2001-04-12512004-07-252090
    2001-11-17342004-11-0163
    2001-11-22252005-01-201860
    2002-03-15132005-08-22337
    2002-04-2125202005-09-091880
    2002-07-15234
    下载: 导出CSV

    表  5  集成模型在测试集上的预报结果

    Table  5.   Result of the ensemble model on the test set

    预报/实际无SPE有SPE总计
    无SPE1394143
    有SPE41721
    总计14321164
    下载: 导出CSV

    表  6  本文模型与现行其他预报方法的对比

    Table  6.   Comparison with other current prediction methods

    模型报准率虚报率F1
    PPS85.71%50.00%0.6316
    Laurenza模型63%42%0.6040
    UMASEP80.72%33.99%0.7263
    Qiuzhen模型80.00%25.85%0.7696
    本文方法80.95%19.05%0.8085
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-03-09
  • 录用日期:  2021-05-17
  • 修回日期:  2022-04-01
  • 网络出版日期:  2022-05-23

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