Recurrent Neural Networks based on LSTM for Predicting Geomagnetic Field

Tong Liu, Tailin Wu, Meiling Wang, Mengyin Fu, Jiapeng Kang, Haoyuan Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

30 Citations (Scopus)

Abstract

The predicting accuracy of geomagnetic field is a major factor influencing magnetic anomaly detection, geomagnetic navigation and geomagnetism. The limitations of current methods consist of complex model, a large number of parameters, method of solving parameters with high complexity and low forecast accuracy during geomagnetic disturbed days. In this paper we explore a deep learning method for forecasting geomagnetic field that adopts structure of recurrent neural networks (RNN) based on long-short term memory (LSTM). This method of LSTM RNN includes analyzing the characteristics of geomagnetic field and training the data set of geomagnetic data with simple and robust mathematical model. Compared with current methods, the high-precision prediction of geomagnetic field based on LSTM RNN is achieved during both geomagnetic quiet and disturbed days. Furthermore, it could be found that the average error and maximum error of LSTM RNN are far smaller than those of the other methods.

Original languageEnglish
Title of host publicationICARES 2018 - Proceedings of the 2018 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages56-60
Number of pages5
ISBN (Electronic)9781538660324
DOIs
Publication statusPublished - 26 Nov 2018
Event2018 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2018 - , Indonesia
Duration: 20 Sept 201821 Sept 2018

Publication series

NameICARES 2018 - Proceedings of the 2018 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology

Conference

Conference2018 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2018
Country/TerritoryIndonesia
Period20/09/1821/09/18

Keywords

  • Geomagnetic Field
  • High-precision Prediction
  • Long-Short Term Memory
  • Recurrent Neural Networks

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Liu, T., Wu, T., Wang, M., Fu, M., Kang, J., & Zhang, H. (2018). Recurrent Neural Networks based on LSTM for Predicting Geomagnetic Field. In ICARES 2018 - Proceedings of the 2018 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (pp. 56-60). Article 8547087 (ICARES 2018 - Proceedings of the 2018 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICARES.2018.8547087