Volume 42 Issue 3
Jun.  2022
Turn off MathJax
Article Contents
Article Navigation >  Chinese Journal of Space Science  > 2022  >  42(3): 340-345
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
shu

Solar Proton Events Short-time Forecasting Based on Ensemble Learning

doi: 10.11728/cjss2022.03.210310025
  • 1.

    National Space Science Center, Chinese Academy of Sciences, Beijing 100190

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049

  • Received Date: 2021-03-09
  • Accepted Date: 2021-05-17
  • Rev Recd Date: 2022-04-01
    • Available Online: 2022-05-23
    Abstract
  • Solar proton event is a space weather phenomenon caused by energetic particles ejected and propagated into near-Earth space during bursts of solar activity. These high-energy particles can cause serious harm to spacecraft and astronauts, therefore, accurate short-term forecasting of solar proton events is very necessary as part of disaster prevention for space activities. The short-time forecasting of solar proton events still faces a lot of challenges, one of which is the high false alarm rate. To solve this problem, we adopted a whole new set of methods-machine learning. As a branch of computer science, many excellent algorithms have emerged in the field of machine learning in recent years, and have achieved successful applications in many fields. In this study, an ensemble model based on 8 widely used machine learning models is established to make precise forecasting of solar proton events. An experiment on the 23rd solar cycle shows that this model gets a probability of detection of 80.95% and a false alarm rate of 19.05%.

     

    • FullText(HTML)
  • loading
    • References(25)
  • [1]
    ARAN A, SANAHUJA B, LARIO D. SOLPENCO: a solar particle engineering code[J]. Advances in Space Research, 2006, 37(6): 1240-1246 doi: 10.1016/j.asr.2005.09.019
    [2]
    LUHMANN J G, LEDVINA S A, ODSTRCIL D, et al. Cone model-based SEP event calculations for applications to multipoint observations[J]. Advances in Space Research, 2010, 46(1): 1-21 doi: 10.1016/j.asr.2010.03.011
    [3]
    KAHLER S W, CLIVER E W, LING A G. Validating the Proton Prediction System (PPS)[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2007, 69(1/2): 43-49
    [4]
    LAURENZA M, CLIVER E W, HEWITT J, et al. A technique for short-term warning of solar energetic particle events based on flare location, flare size, and evidence of particle escape[J]. Space Weather, 2009, 7(4): S04008
    [5]
    NÚÑEZ M. Predicting solar energetic proton events (E > 10 MeV)[J]. Space Weather, 2011, 9(7): S07003
    [6]
    HUANG X, WANG H N, LI L P. Ensemble prediction model of solar proton events associated with solar flares and coronal mass ejections[J]. Research in Astronomy and Astrophysics, 2012, 12(3): 313-321 doi: 10.1088/1674-4527/12/3/007
    [7]
    ZHONG Q Z, WANG J J, MENG X J, et al. Prediction model for solar energetic proton events: analysis and verification[J]. Space Weather, 2019, 17(5): 709-726 doi: 10.1029/2018SW001915
    [8]
    KAHLER S W. The role of the big flare syndrome in correlations of solar energetic proton fluxes and associated microwave burst parameters[J]. Journal of Geophysical Research: Space Physics, 1982, 87(A5): 3439-3448 doi: 10.1029/JA087iA05p03439
    [9]
    乐贵明, 王鸿雁, 白铁男. 太阳质子事件与太阳耀斑的关系[J]. 空间科学学报, 2018, 38(4): 437-443 doi: 10.11728/cjss2018.04.437

    LE Guiming, WANG Hongyan, BAI Tienan. Relationship between solar proton events and the associated solar flares[J]. Chinese Journal of Space Science, 2018, 38(4): 437-443 doi: 10.11728/cjss2018.04.437
    [10]
    KAHLER S W, SHEELEY N R JR, HOWARD R A, et al. Associations between coronal mass ejections and solar energetic proton events[J]. Journal of Geophysical Research: Space Physics, 1984, 89(A11): 9683-9693 doi: 10.1029/JA089iA11p09683
    [11]
    王聪, 崔延美, 敖先志, 等. 全晕CME与太阳质子事件的关系[J]. 空间科学学报, 2018, 38(1): 9-18 doi: 10.11728/cjss2018.01.009

    WANG Cong, CUI Yanmei, AO Xianzhi, et al. Relationship of halo CME and solar proton events[J]. Chinese Journal of Space Science, 2018, 38(1): 9-18 doi: 10.11728/cjss2018.01.009
    [12]
    FALCO M, COSTA P, ROMANO P. Solar flare forecasting using morphological properties of sunspot groups[J]. Journal of Space Weather and Space Climate, 2019, 9: A22 doi: 10.1051/swsc/2019019
    [13]
    MCCLOSKEY A E, GALLAGHER P T, BLOOMFIELD D S. Flare forecasting using the evolution of McIntosh sunspot classifications[J]. Journal of Space Weather and Space Climate, 2018, 8: A34 doi: 10.1051/swsc/2018022
    [14]
    HOWARD T. Coronal Mass Ejections[M]. New York: Springer, 2011
    [15]
    白铁男, 乐贵明, 赵浩峰. 第23至24周太阳质子事件的统计特征[J]. 空间科学学报, 2017, 37(6): 649-658 doi: 10.11728/cjss2017.06.649

    BAI Tienan, LE Guiming, ZHAO Haofeng. Statistical properties of solar proton events during solar cycle 23 and 24[J]. Chinese Journal of Space Science, 2017, 37(6): 649-658 doi: 10.11728/cjss2017.06.649
    [16]
    MCINTOSH P S. The classification of sunspot groups[J]. Solar Physics, 1990, 125(2): 251-267 doi: 10.1007/BF00158405
    [17]
    徐振中, 王伟民, 张韧, 等. 第23太阳活动周武汉站电离层TEC特征分析[J]. 空间科学学报, 2013, 33(1): 28-33 doi: 10.11728/cjss2013.01.028

    XU Zhenzhong, WANG Weimin, ZHANG Ren, et al. Characteristic analysis of ionosphere TEC at Wuhan station during 23 rd solar cycle[J]. Chinese Journal of Space Science, 2013, 33(1): 28-33 doi: 10.11728/cjss2013.01.028
    [18]
    HARRISON E, RIINU P. Logistic regression[M]// R for Health Data Science. New York: Chapman and Hall/CRC, 2020.
    [19]
    WANG P H, TU Y S, TSENG Y J. PgpRules: a decision tree based prediction server for P-glycoprotein substrates and inhibitors[J]. Bioinformatics, 2019, 35(20): 4193-4195 doi: 10.1093/bioinformatics/btz213
    [20]
    FEZAI R, BOUZRARA K, MANSOURI M, et al. Random forest-based nonlinear improved feature extraction and selection for fault classification[C]//Proceedings of 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD). Monastir: IEEE, 2021: 601-606
    [21]
    FREUND Y, SCHAPIRE R E. A decision-theoretic generalization of on-line learning and an application to boosting[J]. Journal of Computer and System Sciences, 1997, 55(1): 119-139 doi: 10.1006/jcss.1997.1504
    [22]
    BIAU G, CADRE B, ROUVIÈRE L. Accelerated gradient boosting[J]. Machine Learning, 2019, 108(6): 971-992 doi: 10.1007/s10994-019-05787-1
    [23]
    CHEN T Q, GUESTRIN C. XGBoost: a scalable tree boosting system[C]//Proceedings of the 22 nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM, 2016: 785-794
    [24]
    ZHU H J, ZHU L H. Encrypted network behaviors identification based on dynamic time warping and k-nearest neighbor[J]. Cluster Computing, 2019, 22(2): 2571-2580
    [25]
    PÉREZ A, LARRAÑAGA P, INZA I. Supervised classification with conditional Gaussian networks: increasing the structure complexity from naive Bayes[J]. International Journal of Approximate Reasoning, 2006, 43(1): 1-25 doi: 10.1016/j.ijar.2006.01.002
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 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

    Tables(6)

    Get Citation
    PDF
    XML

    Article Metrics

    Article Views(207) PDF Downloads(53) Cited by()
    Proportional views
    Related

    Journal Introduction

    ISSN 0254-6124       CN 11-1783/V

    Copyright © Editorial Office of Chinese Journal of Space Science.  京ICP备08100722号

    Supported by: Beijing Renhe Information Technology Co., Ltd.  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return