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Solar Energetic Particle events (SEPs) are among the most dangerous transientphenomena of solar activity. As hazardous radiation, SEPs may affect the healthof astronauts in outer space and adversely impact current and future spaceexploration. In this paper, we consider the problem of daily prediction ofSolar Proton Events (SPEs) based on the characteristics of the magnetic fieldsin solar Active Regions (ARs), preceding soft X-ray and proton fluxes, andstatistics of solar radio bursts. The machine learning (ML) algorithm uses anartificial neural network of custom architecture designed for whole-Sun input.The predictions of the ML model are compared with the SWPC NOAA operationalforecasts of SPEs. Our preliminary results indicate that 1) for the AR-basedpredictions, it is necessary to take into account ARs at the western limb andon the far side of the Sun; 2) characteristics of the preceding proton fluxrepresent the most valuable input for prediction; 3) daily mediancharacteristics of ARs and the counts of type II, III, and IV radio bursts maybe excluded from the forecast without performance loss; and 4) ML-basedforecasts outperform SWPC NOAA forecasts in situations in which missing SPEevents is very undesirable. The introduced approach indicates the possibilityof developing robust "all-clear" SPE forecasts by employing machine learningmethods.

Prediction of Solar Proton Events with Machine Learning: Comparison with Operational Forecasts and "All-Clear" Perspectives