Volume 40 Issue 5
Sep.  2020
Turn off MathJax
Article Contents
Article Navigation >  Chinese Journal of Space Science  > 2020  >  40(5): 875-883
LIU Siqing, CHEN Yanhong, LUO Bingxian, CUI Yanmei, ZHONG Qiuzhen, WANG Jingjing, YUAN Tianjiao, HU Qinghua, HUANG Xin, CHEN Hong. Development of New Capabilities Using Machine Learning for Space Weather Prediction[J]. Chinese Journal of Space Science, 2020, 40(5): 875-883. doi: 10.11728/cjss2020.05.875
Citation: LIU Siqing, CHEN Yanhong, LUO Bingxian, CUI Yanmei, ZHONG Qiuzhen, WANG Jingjing, YUAN Tianjiao, HU Qinghua, HUANG Xin, CHEN Hong. Development of New Capabilities Using Machine Learning for Space Weather Prediction[J]. Chinese Journal of Space Science, 2020, 40(5): 875-883. doi: 10.11728/cjss2020.05.875
shu

Development of New Capabilities Using Machine Learning for Space Weather Prediction

doi: 10.11728/cjss2020.05.875

  • 1. National Space Science Center, Chinese Academy of Sciences, Beijing 100190;

  • 2. Key Laboratory of Science and Technology on Environmental Space Situation Awareness, Chinese Academy of Sciences, Beijing 100190;

  • 3. University of Chinese Academy of Sciences, Beijing 100049;

  • 4. School of Computer Science and Technology, Tianjin University, Tianjin 300072;

  • 5. Key Laboratory of Solar Activity, National Astronomical Observatories of Chinese Academy of Sciences, Beijing 100101;

  • 6. College of Science, Huazhong Agricultural University, Wuhan 430070

Funds:

Supported by National Natural Science Foundation of China (41574181)

More Information
  • Author Bio:

    CHEN Yanhong,E-mail:chenyh@nssc.ac.cn

  • Received Date: 2020-03-15
  • Publish Date: 2020-09-15
    Abstract
  • With the development of space exploration and space environment measurements, the numerous observations of solar, solar wind, and near Earth space environment have been obtained in last 20 years. The accumulation of multiple data makes it possible to better use machine learning technique, which has achieved unforeseen results in industrial applications in last decades, for developing new approaches and models in space weather investigation and prediction. In this paper, the efforts on the forecasting methods for space weather indices, events, and parameters using machine learning are briefly introduced based on the study works in recent years. These investigations indicate that machine learning, especially deep learning technique can be used in automatic characteristic identification, solar eruption prediction, space weather forecasting for solar and geomagnetic indices, and modeling of space environment parameters.

     

    • FullText(HTML)
  • loading
    • References(14)
  • [1]
    FANG Y, CUI Y, AO X, Deep learning for automatic recognition of magnetic type in sunspot groups[J]. Adv. Astron., 2019:9196234
    [2]
    HUANG X, WANG H, XU L, et al. Deep learning based solar flare forecasting model. I. Results for line-of-sight magnetograms[J]. Astrophys. J., 2019, 856:7
    [3]
    WANG J, LIU S, AO X, et al. Parameters derived from the SDO/HMI vector magnetic field data:potential to improve machine-learning-based solar flare prediction models[J]. Astrophys. J., 2019, 884:175
    [4]
    WANG J, LIU S, AO X, et al. Solar flare predictive features derived from polarity inversion line masks in active regions using an unsupervised machine learning algorithm[J]. Astrophys. J., 2020, 92:140
    [5]
    WANG Z, HU Q, ZHONG Q, WANG Y. Linear multistep F10.7 forecasting based on task correlation and heteroscedasticity[J]. Earth Space Sci., 2018, 5:863-874
    [6]
    LIU Y, LUO B, LIU S, GONG J. Kp Forecast models based on neural networks[J]. Manned Spaceflight, 2013, 19(2):70-80
    [7]
    TAN Y, HU Q, WANG Z, ZHONG Q. Geomagnetic index Kp forecasting with LSTM[J]. Space Weather, 2018, 16:406-416
    [8]
    GRAVES A. Supervised Sequence Labelling with Recurrent Neural Networks[M]. Heidelberg:Springer, 2012
    [9]
    BOYLE C, REIFF P, HAIRSTON M. Empirical polar cap potentials[J]. J. Geophys. Res., 1997, 102(A1):111-125
    [10]
    CHEN Y, XUE B, LI L. Forecasting of ionospheric critical frequency using neural networks[J]. Chin. J. Space Sci., 2005, 25(2):99-103
    [11]
    CHEN C, WU Z, SUN S, et al. Forecasting of ionospheric f0F2 in china using neural network[J]. Chin. J. Space Sci., 2011, 31(3):304-310
    [12]
    YUAN T, CHEN Y, LIU S, GONG J. Prediction model for Ionospheric total electron content based on deep learning recurrent neural network[J]. Chin. J. Space Sci., 2018, 38(1):48-57
    [13]
    LANZEROTTI L J, LAFLEUR K, MACLENNAN C G, MAURER D W. Studies of spacecraft charging on a geosynchronous telecommunications satellite[J]. Adv. Space Res., 1998, 22(1):79-82
    [14]
    WEI L, ZHONG Q, LIN R et al. Quantitative prediction of high-energy electron integral flux at geostationary orbit based on deep learning[J]. Space Weather, 2018, 16:903-916
    • 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
    Get Citation
    PDF
    XML

    Article Metrics

    Article Views(634) PDF Downloads(55) 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