Volume 42 Issue 2
Mar.  2022
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Article Navigation >  Chinese Journal of Space Science  > 2022  >  42(2): 199-205
WANG Zisiyu, SHI Liqin, LIU Siqing, ZHONG Qiuzhen, CHEN Yanhong, YAN Xiaohui, SHI Yurong, HE Xinran. Kp Index Prediction Based on Similarity Algorithm of Machine Learning (in Chinese). Chinese Journal of Space Science, 2022, 42(2): 199-205. DOI: 10.11728/cjss2022.02.210316030
Citation: WANG Zisiyu, SHI Liqin, LIU Siqing, ZHONG Qiuzhen, CHEN Yanhong, YAN Xiaohui, SHI Yurong, HE Xinran. Kp Index Prediction Based on Similarity Algorithm of Machine Learning (in Chinese). Chinese Journal of Space Science, 2022, 42(2): 199-205. DOI: 10.11728/cjss2022.02.210316030
shu

Kp Index Prediction Based on Similarity Algorithm of Machine Learning

doi: 10.11728/cjss2022.02.210316030
  • 1.

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

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049

  • 3.

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

  • Received Date: 2021-03-15
  • Accepted Date: 2021-05-21
  • Rev Recd Date: 2021-12-19
    • Available Online: 2022-05-25
    Abstract
  • The solar wind is the direct cause of the geomagnetic disturbance. In this paper, based on the feature selection and similarity algorithm of machine learning, a recommended model is established to search for cases whose characteristics are similar to the current solar wind in historical solar wind data, and to obtain the prediction of the geomagnetic Kp index. Tested on 120 solar wind cases randomly selected from 1998 to 2019, the results show that the solar wind cases which have similar geomagnetic effects to the input solar wind can be worked out successfully by proposed model . And the root mean square error between the Kp index of the optimal case recommended by the model and the actual value is 0.79, and the correlation coefficient is 0.93. Different from traditional forecast models, the proposed recommended model in this paper can not only provide a geomagnetic Kp index as a forecast, but also give a clearer and more intuitive comparison of the changes between the solar wind characteristic parameters according to the time series. Even because the historical events have already happened, we can artificially find more dimensional information of the similar historical cases, which makes forecasters better combine their own experience in Kp index forecasting.

     

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