Journal of Geodesy and Geoinformation Science ›› 2020, Vol. 3 ›› Issue (2): 1-15.doi: 10.11947/j.JGGS.2020.0201

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An Investigation of Optimal Machine Learning Methods for the Prediction of ROTI

Fulong XU1,Zishen LI3(),Kefei ZHANG1,2(),Ningbo WANG3,Suqin WU2,Andong HU4,Lucas Holden2   

  1. 1. School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221116, China
    2. Satellite Positioning for Atmosphere, Climate and Environment (SPACE)Research Centre, Royal Melbourne Institute of Technology (RMIT)University, Melbourne VIC 3001, Australia
    3. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 10094, China
    4. Centrum Wiskunde & Informatica (CWI), P.O. Box 940791090 GB Amsterdam NETHERLANDS
  • Received:2020-01-14 Accepted:2020-04-14 Online:2020-06-20 Published:2020-07-07
  • Contact: Zishen LI,Kefei ZHANG E-mail:lizishen@aircas.ac.cn(Z.L.);Kefei.zhang@rmit.edu.au(K.Z)
  • About author:Fulong XU(1995—), male, master degree candidate, majors in high latitude ionospheric scintillation based on deep learning.E mail: fulong_xu@cumt.edu.cn
  • Supported by:
    National Key Research Program of China(2017YFE0131400);National Natural Science Foundation of China(41674043);National Natural Science Foundation of China(41704038);National Natural Science Foundation of China(41874040);Beijing Nova Program(xx2017042);Beijing Talents Foundation(2017000021223ZK13);CUMT Independent Innovation Project of “Double-First Class” Construction(2018ZZ08)

Abstract:

The rate of the total electron content (TEC)change index (ROTI)can be regarded as an effective indicator of the level of ionospheric scintillation, in particular in low and high latitude regions. An accurate prediction of the ROTI is essential to reduce the impact of the ionospheric scintillation on earth observation systems, such as the global navigation satellite systems. However, it is difficult to predict the ROTI with high accuracy because of the complexity of the ionosphere. In this study, advanced machine learning methods have been investigated for ROTI prediction over a station at high-latitude in Canada. These methods are used to predict the ROTI in the next 5 minutes using the data derived from the past 15 minutes at the same location. Experimental results show that the method of the bidirectional gated recurrent unit network (BGRU)outperforms the other six approaches tested in the research. It is also confirmed that the RMSEs of the predicted ROTI using the BGRU method in all four seasons of 2017 are less than 0.05 TECU/min. It is demonstrated that the BGRU method exhibits a high level of robustness in dealing with abrupt solar activities.

Key words: machine learning; ROTI prediction; ionospheric scintillation; high-latitude region