IJCATR Volume 13 Issue 8

Enhanced Prediction of Ionospheric Total Electron Content Using Deep Learning Model Over Equatorial Kenya: A Review of Literature

Athman Masoud, Mvurya Mgala, Kennedy Hadullo
10.7753/IJCATR1308.1009
keywords : Deep Learning; Predictions; LSTM; Ionospheric; Total Electron Content; Space Weather Forecasting

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This study explores the emergence of deep learning approach in predicting ionospheric total electron content (TEC); which is pivotal for space weather forecasting. Total electron content variability has profound implications for satellite communications and navigation systems, especially in regions with unique ionospheric characteristics such as equatorial Kenya. Traditional TEC prediction models which are rooted in empirical or physics-based methods, often encounter challenges in capturing the complex, non-linear behaviors inherent in equatorial ionospheric dynamics. A systematic literature review (SLR) was performed to extract and synthesize the algorithms and features that have been used in long short-term memory networks (LSTMs) techniques to model and predict the ionospheric TEC, with a focus on the unique characteristics of the geomagnetic equator at low latitudes. Several articles on ionospheric TEC prediction using deep learning were obtained from research databases where few were selected based on the inclusion/exclusion criteria used in the study. The outcome of the review from several studies embarked on deep learning using LSTMs architectures, they highlighted the importance of feature selection and feature engineering in enhancing prediction accuracy. The study also explored hybrid machine learning techniques models which showed improved forecast performance. Additionally, the suggestion on addressing data gaps and considering additional parameters which could further enhance the accuracy and reliability of TEC predictions.
@artical{a1382024ijcatr13081009,
Title = "Enhanced Prediction of Ionospheric Total Electron Content Using Deep Learning Model Over Equatorial Kenya: A Review of Literature",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "13",
Issue ="8",
Pages ="90 - 94",
Year = "2024",
Authors ="Athman Masoud, Mvurya Mgala, Kennedy Hadullo"}
  • The paper is a systematic review of literature on the deep learning techniques.
  • Explores specifically combinations of long- short term memory LSTM algorithms.
  • Uncovers various features for prediction of total electron content (TEC).
  • Data gaps was discovered as an area of future research for TEC prediction.