Global hmF2 Parameter Prediction Modeling Based on COSMIC Satellite Data and SHAP Interpretable Method

Accurately predicting the peak height of the F2 layer (hmF2) is crucial for radio communications, satellite navigation, and space weather studies, yet traditional empirical models often lack precision. To address this, we developed a global hmF2 prediction model using a Multilayer Perceptron (MLP) neural network, based on COSMIC radio occultation observations from 2007 to 2023. Evaluated on independent test sets from 2014 and 2019, the MLP model achieved correlation coefficients of 0.877 and 0.853 with root mean square errors of 22.3 km and 19.1 km, respectively, significantly outperforming the machine learning approaches like XGBoost and Transformer. The model also demonstrated strong generalization on an independent validation set constructed from 2014 and 2019 GIRO data, with a correlation of 0.785 and RMSE of 26.1 km, surpassing NPHM, and XGBoost. SHAP interpretability analysis identified geographic latitude, cosine of latitude, solar F10.7 index, and annual/daily harmonic terms as the most influential physical features. Error analysis showed a mean prediction error of −3.2 km and a standard deviation of 21.8 km, with stable performance during quiet periods and larger errors primarily during disturbed conditions. This study provides a reliable tool for high-accuracy hmF2 prediction and enhances the understanding of the physical mechanisms controlling its variability.