Abstract

The ability to predict the ionosphere activity is of interest for several applications such as satellite telecommunications or Global Navigation Satellite Systems (GNSS). A few studies have proposed models able to predict Total Electron Content (TEC) values of the ionosphere locally over measuring stations, but not worldwide for most of them. We propose a method using Deep Neural Networks (DNN) to predict a sequence of global TEC maps consecutive to an input sequence of past TEC maps, by combining Convolutional Neural Networks (CNNs) with convolutional Long Short-Term Memory (LSTM) networks. The numerical experiments show that the approach provides signi cant improvement over methods implemented for benchmarking and is competitive with state-of-the-art methods while providing global TEC predictions. The proposed architecture can be adapted to any sequence-to-sequence prediction problem.

Citation

@inproceedings{cherrier2017deep,
  title={Deep sequence-to-sequence neural networks for ionospheric activity map prediction},
  author={Cherrier, No{\"e}lie and Castaings, Thibaut and Boulch, Alexandre},
  booktitle={International Conference on Neural Information Processing},
  pages={545--555},
  year={2017},
  organization={Springer}
}