Volume 41 Issue 3
May  2021
Turn off MathJax
Article Contents
XIONG Senlin, LI Xinlu, FANG Shaofeng, ZOU Ziming. Research on Solar Proton Event in the 23rd Solar Cycle Using the Machine Learning Methods[J]. Chinese Journal of Space Science, 2021, 41(3): 368-374. doi: 10.11728/cjss2021.03.368
Citation: XIONG Senlin, LI Xinlu, FANG Shaofeng, ZOU Ziming. Research on Solar Proton Event in the 23rd Solar Cycle Using the Machine Learning Methods[J]. Chinese Journal of Space Science, 2021, 41(3): 368-374. doi: 10.11728/cjss2021.03.368

Research on Solar Proton Event in the 23rd Solar Cycle Using the Machine Learning Methods

doi: 10.11728/cjss2021.03.368
  • Received Date: 2019-12-09
  • Rev Recd Date: 2020-08-24
  • Publish Date: 2021-05-15
  • Solar Proton Event (SPE) can pose crucial risks to the spacecraft. It is meaningful to analyze and build the relationships between SPE and the associated Coronal Mass Ejection (CME) and solar flares. In this study, the SPE in the 23rd solar cycle is investigated by using machine learning methods. Datasets were constructed based on CME and the solar proton events lists from 1997 to 2006 from the CDA web database. Apriori algorithm are used to survey the correlations between SPEs and the characteristics of flares and CME. The results show that X class flares, full halo CME, high speed (greater than 1000km·-1) CME, and western flares are the four characteristics that most likely to be associated with SPE. The corresponding probabilities are 0.366, 0.355, 0.30 and 0.155. The SPE probabilities at the condition of more than one (CME or flare) features occurring simultaneously were exhibited as well. Using the over sampled CME and flares features, five SPE prediction models are built through five different supervised machine learning algorithms, thus Logistic Regression, Support Vector Classification, K-nearest neighbor, Random Forest and Gradient Boosting Decision Tree. The models all present pretty good prediction accuracy (>0.94), precision (>0.96) and recall rate (>0.91).

     

  • loading
  • [1]
    BALCH C C. SEC proton prediction model: verification and analysis[J]. Radiat. Meas., 1999, 30(3):231-250
    [2]
    DESAI M I, GIACALONE J. Large gradual solar energetic particle events[J]. Living Rev. Sol. Phys., 2016, 13(1):1-132
    [3]
    REAMES D V. The two sources of solar energetic particles[J]. Space Sci. Rev., 2013, 175(1):53-92
    [4]
    DIERCKXSENS M, TZIOTZIOU K, DALLA S, et al. Relationship between solar energetic particles and properties of flares and CMEs: statistical analysis of solar cycle 23 events[J]. Sol. Phys., 2015, 290(3):841-874
    [5]
    PARK J, MOON Y J, LEE D H, et al. Dependence of solar proton events on their associated activities: flare parameters[J]. J. Geophys. Res., 2010, 115(1):39
    [6]
    PARK J, MOON Y J, GOPALSWAMY N. Dependence of solar proton events on their associated activities: coronal mass ejection parameters[J]. J. Geophys. Res., 2012, 117(A8):108-115
    [7]
    XIONG Senlin, CUI Yanmei, LIU Siqing. Research on solar proton event warning with observation data of ACE satellite[J]. Chin. J. Space Sci., 2013, 33(4):387-395(熊森林, 崔延美, 刘四清. 利用ACE卫星数据对太阳质子事件预警方法的研究[J]. 空间科学学报, 2013, 33(4):387-395)
    [8]
    YASHIRO S, AKIYAMA S, GOPALSWAMY N, et al. Different power-law indices in the frequency distributions of flares with and without coronal mass ejections[J]. Astrophys. J., 2006, 650(2):143-146
    [9]
    CHAWLA N V, BOWYER K W, HALL L O, et al. Smote: synthetic minority over-sampling technique[J]. J. Artif. Intell. Res., 2002, 16(1):321-357
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article Views(542) PDF Downloads(70) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return