Ionospheric Scintillation Prediction Model at Low Latitude Station Investigating a Machine Learning Technique
Salah, H., Okoh, D., Yousef, M., Mahrous, A. (2024). Ionospheric Scintillation Prediction Model at Low Latitude Station Investigating a Machine Learning Technique. EKB Journal Management System, 2(1), 46-52. doi: 10.21608/abas.2023.245790.1037
Hager M. Salah; Daniel Okoh; Mohamed Yousef; Ayman Mahrous. "Ionospheric Scintillation Prediction Model at Low Latitude Station Investigating a Machine Learning Technique". EKB Journal Management System, 2, 1, 2024, 46-52. doi: 10.21608/abas.2023.245790.1037
Salah, H., Okoh, D., Yousef, M., Mahrous, A. (2024). 'Ionospheric Scintillation Prediction Model at Low Latitude Station Investigating a Machine Learning Technique', EKB Journal Management System, 2(1), pp. 46-52. doi: 10.21608/abas.2023.245790.1037
Salah, H., Okoh, D., Yousef, M., Mahrous, A. Ionospheric Scintillation Prediction Model at Low Latitude Station Investigating a Machine Learning Technique. EKB Journal Management System, 2024; 2(1): 46-52. doi: 10.21608/abas.2023.245790.1037

Ionospheric Scintillation Prediction Model at Low Latitude Station Investigating a Machine Learning Technique

Advances in Basic and Applied Sciences
Article 4, Volume 2, Issue 1, January 2024, Page 46-52  XML PDF (764.87 K)
Document Type: Original Article
DOI: 10.21608/abas.2023.245790.1037
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Authors
Hager M. Salah email orcid 1, 2; Daniel Okoh3, 4; Mohamed Yousef1; Ayman Mahrous1, 5
1Space Weather Monitoring Centre (SWMC), Faculty of Science, Helwan University
2Canadian International College in Cairo, Cairo, Egypt
3United Nations African Regional Centre for Space Science and Technology Education – English (UN-ARCSSTE-E), Obafemi Awolowo University Campus, Ile Ife, Nigeria
4National Institute of Geophysics and Volcanology, Rome, Italy
55Department of Space Environment, Institute of Basic and Applied Science, Egypt-Japan University of Science and Technology 21934 Alexandria, Egypt
Abstract
Ionospheric scintillation forecasting and modeling are vital for efficiently tracking satellites and navigation systems. Scintillations modulate the amplitude or phase of a signal waveform caused by abnormalities of the ionospheric electron density. These fluctuating signals can cause cycle slips, disconnect the receiver signal, and cause lock loss. In the current article, we predict the amplitude of scintillation (S4 index) using a machine-learning approach. A feedforward backpropagation technique was implemented. For further learning of models regarding the dynamics of the ionospheric F layer, we inserted foF2 and hmF2 parameters in the input layer neurons. The ground–based SCINDA data at Helwan, Egypt (29.86° N, 31.32° E) from 2009 to 2017 has been considered. The results show that predicted S4 values closely reflect observed S4 values for different conditions of the solar cycle 24, with a RMSE of 0.019 and regression of 0.659. The variations of ionospheric scintillation near the equatorial anomaly's northern peak have also been conducted during different levels of solar cycle 24 based on the ANN.
Keywords
Ionospheric Scintillation; Equatorial ionization anomaly; GNSS; Machine Learning; Feedforward Backpropagation
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