Prediction of Solar Flares and Background Fluxes of X-Ray Radiation According to Synoptic Ground-Based Observations Using Machine-Learning Models

Abstract

The paper presents machine-learning models for predicting powerful solar flares and background X-ray fluxes in the range of 1–8 A. To predict solar flares for the next day, information was used on the current level of solar activity obtained from ground-based synoptic observations, such as characteristics of sunspots and radio fluxes at wavelengths of 10.7 and 5 cm, as well as the level of the background flux and the number of solar flares of the current day obtained from the GOES satellite. To predict the background fluxes of X-ray radiation, only data from ground-based telescopes were used. The high efficiency of the forecast for the next day is shown. The neural network was trained on data available since 2002.