Akademik Veri Yönetim Sistemi
Turkish Physical Society 41th International Physics Congress, Muğla, Türkiye, 1 - 05 Eylül 2025, ss.253, (Özet Bildiri)
Accurate prediction of the Total Electron Content (TEC) in Earth's ionospheric layer,
which constitutes a natural plasma environment, is critical for mitigating the adverse
effects of space weather events on technological systems.
This study aims to develop a high-accuracy prediction model by using input parameters
including the Dst, Kp, and F10.7 Solar Flux indices, the solar position, and historical TEC
data. In this context, mid-latitude TEC measurements obtained from the ionolab service,
along with relevant space weather indices, were used to train ensemble-based machine
learning models.
The performance of the models was evaluated using standard statistical metrics such as
Root Mean Square Error (RMSE) and the Coefficient of Determination (R2). The results
demonstrate that machine learning techniques offer reliable tools with the potential for
operational implementation in TEC forecasting.
Our future work will focus on analyzing the performance of this successful model in high-
latitude polar regions.