An Explainable Dynamic Prediction Method for Ionospheric foF2 Based on Machine Learning

Abstract

To further improve the prediction accuracy of the critical frequency of the ionospheric F2 layer (foF2), we use the machine learning method (ML) to establish an explanatory dynamic model to predict foF2. Firstly, according to the ML modeling process, the three elements of establishing a prediction model of foF2 and four problems to be solved are determined, and the idea and concrete steps of model building are determined. Then the data collection is explained in detail, and according to the modeling process, foF2 dynamic change mapping and its parameters are determined in turn. Finally, the established model is compared with the International Reference Ionospheric model (IRI-2016) and the Asian Regional foF2 Model (ARFM) to verify the validity and reliability. The results show that compared with the IRI-URSI, IRI-CCIR, and ARFM models, the statistical average error of the established model decreased by 0.316 MHz, 0.132 MHz, and 0.007 MHz, respectively. Further, the statistical average relative root-mean-square error decreased by 9.62%, 4.05%, and 0.15%, respectively.