Global Ionospheric F-Layer Electron Density Prediction Based on Multiple Radio Occultation Data Using Attention-Based Deep Learning Model

Understanding low-latitude F-layer ionospheric electron density (Ne) under severe geomagnetic conditions is crucial for various GNSS applications. Existing ionospheric models utilizing machine learning (ML) have struggled to accurately capture the complex dynamics of Ne, particularly under extreme geomagnetic conditions. In this study, we propose the attention-based recurrent ResNet18 (ABRR-18) model to predict ionospheric Ne using radio occultation (RO) data obtained from multiple satellite missions between 2002 and 2023. The proposed model integrates ResNet18 and bidirectional-long short-term memory (Bi-LSTM) with a spatial attention mechanism (SAM). Besides, it incorporates various space weather indicators such as solar flux, sunspot number, disturbance storm time, and interplanetary magnetic field (IMF). Experimental results revealed that ABRR-18 outperformed other applied models, such as artificial neural network (ANN)-international reference ionosphere (IRI), ANN-TDD, least-squares boosting (LSBoost), Bi-LSTM, and AlexNet-Bi-LSTM-SAM, achieving a correlation of 0.9674 and a root-mean-square error (RMSE) of 1.0295 \times 10^{5} ele/cm3. ABRR-18 showed superior performance under severe geomagnetic conditions and during high solar activity years over the IRI-2016 model. In addition, the ABRR-18 model outperforms the IRI-2016 and IRI-2020 models, with predictions closely aligning with incoherent scatter radar (ISR) observations, particularly during extreme conditions. Compared to the IRI model (IRI-2016 and IRI-2020), ABRR-18 demonstrated superior accuracy in characterizing global ionospheric spatial–temporal properties. This study underscores the potential of deep learning (DL) techniques in ionospheric modeling by exhibiting superior performance. The ABRR-18 model introduces an innovative approach, offering notable advancements in comprehending and predicting ionospheric Ne in challenging conditions.