Solar Radio Burst Prediction Based on a Multimodal Model

Abstract

Solar radio bursts are intense radio radiation sources that occur during the energy-release process and represent a hot topic in solar-physics and space-weather research. In this paper, we present a multimode prediction model for daily solar radio bursts. The model uses deep learning and machine learning to obtain data information from different dimensions and to establish the relationship between the characteristics of the solar active region on the solar surface and solar radio bursts. For this model, we use data from the Solar and Heliospheric Observatory (SOHO)/Michelson Doppler Imager (MDI) total solar magnetic map, the Royal Observatory of Belgium World Data Centre in Brussels, and NOAA sunspot parameters (including number, area, and type of sunspots) as inputs. The output results are then compared with the list of solar radio bursts recorded by the Radio Solar Telescope Network (RSTN) to determine whether solar radio bursts are present and to determine the key parameters for determining radio bursts. Based on 5449 days of observational data, we find that the prediction accuracy of the model is 0.898 ± 0.011, and that the number of sunspots is a key parameter in determining the occurrence of solar radio bursts. Specifically, when the number of sunspots is greater than 15, the probability of occurrence of solar radio bursts is greater than 90%. We have identified the key parameters and thresholds for determining solar radio bursts and highlighted the key parameters for space-weather prediction. In addition, the prediction model can also be used for predicting in other fields.