3D-DNN Ion-EG Model Applications for Ionospheric Characterization over Egypt: MANS CORS Case Study of VTEC Variability and NmF2/HmF2 Mapping (2020-2024)

This study introduces an operational evaluation of a deep learning–driven ionospheric modeling system tailored for Egypt, designed to overcome the limitations of conventional empirical approaches in data-sparse regions. The 3D-DNN Ion-EG model was trained on 737,110 COSMIC-2 radio occultation profiles (2020–2024), integrating spatial temporal coordinates with geophysical drivers to generate high-resolution threedimensional electron density distributions. Validation against the MANS CORS station (Mansoura University) demonstrated strong predictive skill (R2 = 0.9337; r = 0.970) under independent testing. Performance analysis across diverse geophysical regimes confirmed the model’s advantage in capturing storm-time and solar-driven disturbances, surpassing empirical models that perform best only under quiet conditions. Furthermore, the system effectively traced Solar Cycle 25 F2-layer variability from minimum to increasing activity without parameter recalibration. Its computational efficiency enables real-time deployment with modest hardware. These findings establish Egypt as a pioneer in AI-based ionospheric modeling and provide a transferable framework for low-latitude regions.