Development of New Capabilities Using Machine Learning for Space Weather Prediction

被引:0
|
作者
LIU Siqing [1 ,2 ,3 ]
CHEN Yanhong [1 ,2 ]
LUO Bingxian [1 ,2 ,3 ]
CUI Yanmei [1 ,2 ]
ZHONG Qiuzhen [1 ,2 ,3 ]
WANG Jingjing [1 ,2 ]
YUAN Tianjiao [1 ,2 ]
HU Qinghua [3 ,4 ]
HUANG Xin [4 ,5 ]
CHEN Hong [5 ,6 ]
机构
[1] National Space Science Center, Chinese Academy of Sciences
[2] Key Laboratory of Science and Technology on Environmental Space Situation Awareness,Chinese Academy of Sciences
[3] University of Chinese Academy of Sciences
[4] School of Computer Science and Technology, Tianjin University
[5] Key Laboratory of Solar Activity, National Astronomical Observatories of Chinese Academy of Sciences
[6] College of Science, Huazhong Agricultural University
基金
中国国家自然科学基金;
关键词
D O I
暂无
中图分类号
P45 [天气预报];
学科分类号
0706 ; 070601 ;
摘要
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.
引用
收藏
页码:875 / 883
页数:9
相关论文
共 14 条
  • [1] Supervised sequence labelling with recurrent neural networks. Graves A. Springer . 2012
  • [2] Solar flare predictive features derived from polarity inversion line masks in active regions using an unsupervised machine learning algorithm. WANG J,LIU S,AO X,et al. The Astrophysical Journal . 2020
  • [3] Empirical polar cap potentials. Boyle,C,P.Reiff,M.Hairston. Journal of Geophysical Research . 1997
  • [4] 基于深度学习递归神经网络的电离层总电子含量经验预报模型
    袁天娇
    陈艳红
    刘四清
    龚建村
    [J]. 空间科学学报, 2018, (01) : 48 - 57
  • [5] Studies of spacecraft charging on a geosynchronous telecommunications satellite
    Lanzerotti, LJ
    Breglia, C
    Maurer, DW
    Johnson, GK
    Maclennan, CG
    [J]. SOLAR-TERRESTRIAL RELATIONS: PREDICTING THE EFFECTS ON THE NEAR- EARTH ENVIRONMENT, 1998, 22 (01): : 79 - 82
  • [6] Geomagnetic Index Kp Forecasting With LSTM[J] . Yao Tan,Qinghua Hu,Zhen Wang,Qiuzhen Zhong. &nbspSpace Weather . 2018 (4)
  • [7] 基于神经网络方法的Kp预报模型
    刘杨
    罗冰显
    刘四清
    龚建村
    [J]. 载人航天, 2013, 19 (02) : 70 - 80
  • [8] Deep learning based solar flare forecasting model I.Results for line-of-sight magnetograms. HUANG X,WANG H,XU L,et al. The Astrophysical Journal . 2019
  • [9] 利用神经网络预报中国地区电离层f0F2
    陈春
    吴振森
    孙树计
    丁宗华
    许正文
    班盼盼
    [J]. 空间科学学报, 2011, 31 (03) : 304 - 310
  • [10] 利用神经网络预报电离层f0F2
    陈艳红
    薛炳森
    李利斌
    [J]. 空间科学学报, 2005, (02) : 99 - 103