A random forest-based prediction model for ionospheric f_oF₂ in China and surrounding regions

The square of the critical frequency of the ionospheric F₂ layer (f_oF₂) is proportional to the peak electron density (N_mF₂) and serves as a crucial parameter affecting the performance of Global Navigation Satellite Systems (GNSS). Enhancing the prediction accuracy of f_oF₂ is essential for optimizing GNSS broadcast ionospheric models, thereby improving the positioning accuracy of GNSS. This study develops an ionospheric f_oF₂ prediction model for China and its surrounding regions using the random forest algorithm, based on data from 18 ionosonde stations of the China Research Institute of Radiowave Propagation and COSMIC occultation observations. The model incorporates multiple features, including Universal Time, day of the year, geographic location, solar, and geomagnetic activities. A comparative analysis with the International Reference Ionosphere (IRI-2020) model validates the prediction accuracy of our model. The results indicate that the random forest model reduces the mean absolute error by 14.81% and 17.11%, and the root mean square error by 11.21% and 13.14%, compared to the IRI CCIR and IRI URSI models, respectively. Additionally, the model exhibits superior prediction accuracy under various latitudes, local times, solar, and geomagnetic activity conditions when compared to IRI-2020. This research not only significantly enhances the f_oF₂ prediction accuracy for China and its surrounding regions but also lays a critical foundation for improving the accuracy and reliability of GNSS globally.