  The need of real-time of monitoring and alerting systems for Space Weather
hazards has grown significantly in the last two decades. One of the most
important challenge for space mission operations and planning is the prediction
of solar proton events (SPEs). In this context, artificial intelligence and
machine learning techniques have opened a new frontier, providing a new
paradigm for statistical forecasting algorithms. The great majority of these
models aim to predict the occurrence of a SPE, i.e., they are based on the
classification approach. In this work we present a simple and efficient machine
learning regression algorithm which is able to forecast the energetic proton
flux up to 1 hour ahead by exploiting features derived from the electron flux
only. This approach could be helpful to improve monitoring systems of the
radiation risk in both deep space and near-Earth environments. The model is
very relevant for mission operations and planning, especially when flare
characteristics and source location are not available in real time, as at Mars
distance.
