An Optimized CatBoost Model for Spatiotemporal Prediction of hmF2 in High-Latitude Regions

The peak height of the F2 layer (hmF2) is a key parameter describing the vertical structure of the ionosphere. It is important for high-frequency radio communication planning and space-weather background assessment, particularly at high latitudes. To improve long-term hmF2 prediction, an empirical model-guided CatBoost model is developed. Predictions from the SHU and E-CHAIM empirical models are incorporated as prior predictors, while spatiotemporal periodicity, solar-activity, and geomagnetic-activity indices are jointly considered to represent the primary drivers of hmF2 variability. A two-stage feature selection procedure, combining stability-based selection and correlation-based redundancy pruning, is employed to identify informative and nonredundant features. The proposed model achieves consistently lower errors than both empirical models. Relative to SHU and E-CHAIM, the proposed model achieves relative root mean square error (RMSE) reductions of 22.67% and 17.70%, respectively, and relative mean relative error (MRE) reductions of 23.21% and 17.79%, respectively. Consistent improvements are observed across different time periods, seasons, and solar-activity conditions. The largest performance gains occur during spring and years of high solar activity. These results demonstrate that integrating empirical-model information with machine learning effectively improves the representation of hmF2 variability at high latitudes. The proposed model provides an effective empirical model-guided approach for long-term spatiotemporal prediction of hmF2 in high-latitude regions.