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Deep Sequence-to-Sequence Neural Networks for Ionospheric Activity Map Prediction

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)

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 significant 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.

Keywords

Sequence prediction Neural network Forecasting TEC Ionosphere Deep learning CNN LSTM 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.ONERA, The French Aerospace LabPalaiseauFrance

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