Ionospheric activity prediction using convolutional recurrent neural networks

2018/10/31 by Boulch, Alexandre, Cherrier, Noëlie, Castaings, Thibaut
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.1810.13273

Abstract

The ionosphere electromagnetic activity is a major factor of the quality of satellite telecommunications, Global Navigation Satellite Systems (GNSS) and other vital space applications. Being able to forecast globally the Total Electron Content (TEC) would enable a better anticipation of potential performance degradations. A few studies have proposed models able to predict the TEC locally, but not worldwide for most of them. Thanks to a large record of past TEC maps publicly available, we propose a method based on Deep Neural Networks (DNN) to forecast a sequence of global TEC maps consecutive to an input sequence of TEC maps, without introducing any prior knowledge other than Earth rotation periodicity. By combining several state-of-the-art architectures, the proposed approach is competitive with previous works on TEC forecasting while predicting the TEC globally.

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