Prediction of ionospheric total electron content over low latitude region: Case study in Ethiopia

Abstract

This study mainly focuses on the prediction of the variability of the Total Electron Content (TEC) affecting space and ground-based technological infrastructures vulnerable to extreme space weather events. The impact of solar wind conditions (SWC) can be quantified by measuring the ionosphere’s TEC. We used a machine learning (ML) algorithm based on the Support Vector Machine (SVM), Long Short-Term Memory (LSTM) Neural Network (NN), and International Reference Ionosphere 2016 (IRI 2016) models to predict the hourly TEC over Ethiopia during quiet and storm geomagnetic conditions. We considered TEC data from the Global Positioning System (GPS) stations at ADIS, CURG, and NEGE during the years 2013 to 2016. The results indicated that the SVM model predicted the hourly TEC variation with a root mean square error (RMSE) between 2.136 and 7.923 Total Electron Content Unit (TECU) under different ionospheric conditions. For the LSTM model, the RMSE between the predicted and observed TEC lies between 1.483 and 2.527 TECU. But the empirical IRI 2016 models’ RMSE from GPS TEC is in the range of 4.777 and 14.519 TECU. The statistical study points out the LSTM has the highest prediction accuracy and is the most robust for accurate prediction of ionospheric TEC in comparison to the SVM and IRI 2016 models throughout both storm and quiet periods. The result can be further improved with a lower RMSE (high correlation) by utilizing auxiliary data and increasing the number of stations employed in the study. This research adds fresh insights to deep learning applications in space weather forecasting in the region of interest.