Long short-term memory and gated recurrent neural networks to predict the ionospheric vertical total electron content

Abstract

This paper provides the application of deep learning models such as Long Short-Term Memory (LSTM) and a recently proposed Gated Recurrent Unit (GRU) in forecasting the ionospheric GPS_VTEC, and compare the performance of the results with that of Multilayer Perceptron (MLP) neural networks, GIM_TEC and the IRI-Plas 2017 models. GPS_VTEC time series data estimated from GPS measurement over low latitude equatorial station MAL2 with (geo.lat -2.70NandLong40.190E) located in Kenya is used in this study. The data span from January 1, 2010 to December 31, 2018 which covers 9 years of solar cycle 24. The data from the year 2010 to 2016 is used for the training, while the year 2017 data is used for validation and finally the data in the year 2018 is used to examine the performance of the models during the testing period. The performance of the models is based on statistical parameters such as root-mean-square error (RMSE) and correlation coefficient (R). The GRU unit shows a correlation coefficient of 0.971 with GPS_VTEC and a prediction error of 2.004 TECU while that of LSTM, MLP, GIM_TEC, and IRI-Plas 2017 models are 0.967, 0.951, 0.832 and 0.774 with a prediction error of 2.055 TECU, 2.336 TECU, 5.913 and 16.183 TECU, respectively. This shows that the predictions of the gated system models are better than the MLP, GIM_TEC and IRI-Plas models. Considering the prediction ability of these models to forecast the GPS-VTEC values under an intense Geomagnetic Storm event, it is observed that the GRU unit can achieve the best prediction accuracy and shows a strong performance for the trend prediction of this event more than the LSTM, MLP, GIM_TEC, and IRI-Plas models. In all, the three deep learning models perform better than the GIM_TEC and the IRI-Plas 2017 model.