Athman A. Masoud, Mvurya Magala and Joseph Olwendo

Abstract:

Predicting ionospheric total electron content (TEC) is pivotal for space weather research, with profound implications for satellite communications and navigation systems, especially in regions with unique ionospheric characteristics such as Equatorial Kenya. Traditional TEC prediction models, rooted in empirical or physics-based methods, often encounter challenges in capturing the complex, non-linear behaviors inherent in equatorial ionospheric dynamics. This study presents an innovative approach employing deep learning techniques to forecast ionospheric TEC specifically in the Equatorial Kenya region.

A systematic literature review explores the unique characteristics of the equatorial ionosphere and the challenges associated with TEC prediction in this region. Traditional ionospheric models and empirical approaches are discussed, highlighting their limitations in capturing the complex and dynamic nature of equatorial ionospheric conditions. The emergence of deep learning including recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) as a powerful tool in ionospheric modelling is then investigated, with a focus on its ability to learn intricate patterns and relationships from large datasets.

The outcome of the review of several studies conducted in equatorial regions are analyzed, showcasing the application of various deep learning architectures. The effectiveness of these models in seasonal, and solar-driven variations in TEC is discussed, along with their potential for real-time prediction. Challenges and future directions in enhancing prediction accuracy of deep learning models are identified.

Keywords: Deep Learning, Predictions, Ionospheric Total Electron Content, Technology, Space Weather Forecasting, Sustainable Environment, Equatorial, Kenya