Global Ionospheric Slab Thickness Prediction Model Using XGBoost and Ensemble Learning

Abstract The ionospheric equivalent slab thickness is a key parameter for understanding the plasma distribution in the ionosphere, with direct relevance to satellite navigation, communication, and skywave over‐the‐horizon radar. However, traditional prediction methods often suffer from regional biases, limiting their global applicability. To address this, we develop a novel global prediction model for ionospheric slab thickness by integrating the Extreme Gradient Boosting (XGBoost) algorithm with Ensemble Learning (EL) techniques (XGBoost + EL). This hybrid approach leverages the strong nonlinear modeling capability of XGBoost and the robustness of EL to enhance prediction accuracy and generalization. The model was trained and tested on global occultation data from 2007 to 2020, incorporating key parameters such as vertical total electron content (vTEC), the F2‐layer critical frequency, and solar activity indices. Evaluation results demonstrate the model's high predictive accuracy, achieving a root mean square error (RMSE) of 62.3 km, a mean absolute error (MAE) of 41.5 km, a mean absolute percentage error (MAPE) of 13.1%, and a correlation coefficient ( R ) of 0.904. Notably, our model significantly outperforms the Neustrelitz equivalent Slab Thickness Model (NSTM), reducing RMSE, MAE, and MAPE by 54.8 km, 47.2 km, and 11.8%, respectively. It also shows superior performance over the International Reference Ionosphere (IRI) model, with corresponding reductions of 72.1 km, 57.1 km, and 14.7%. These findings underscore the considerable potential of machine learning for advancing global ionospheric modeling. Future work will focus on model optimization and assessing its applicability across diverse regions and temporal scales to further improve reliability.