Akademik Veri Yönetim Sistemi
1st Intercontinental Geoinformation Days (IGD) , Mersin, Türkiye, 25 - 26 Kasım 2020, ss.1-4
The
ionosphere has some temporal regular changes under the dominant control of the Sun.
The stationary structure of the ionospheric time series (e.g. TEC, foF2) allows
it to be modeled on a specific time. In this study, we tested the performance
of the artificial intelligent (AI) techniques e.g. a machine learning-based
method, autoregressive integrated moving average (ARIMA), and a deep
learning-based method, long short-term memory (LSTM) network to the prediction
of Total Electron Content (TEC) values. The TEC data of six different locations
in low, middle, and high latitudes were selected from the Center for Orbit
Determination in Europe – Global Ionosphere Maps (CODE-GIMs). To show the
performance of the proposed methods during quiet space weather and a severe
geomagnetic storm, we trained the 60 days TEC data (24 data points in one day)
and forecasted the TEC data of the subsequent five days by fitted models with
optimal hyperparameters. The forecasted TEC values were compared with observed
TEC through some statistical metrics (RMS, MAE). The results indicated
that the LSTM is more successful in TEC prediction than ARIMA. This study
brings new insights into the AI techniques in the ionospheric TEC prediction.