Predictions of SEP events by means of a linear filter and layer-recurrent neural network

Solar energetic particle (SEP) modelling has gained great interest in the community, specifically in connection with the safety of crews and the protection of technological systems of spacecraft situated outside the shielding of Earth's magnetosphere. Two models for the prediction of SEP events are presented in this paper. The models are based on a linear filter and on a special type of dynamic artificial neural network known as the layer-recurrent neural network. In this work they use as input the following parameters: the X-ray flare class for flares originating close to the centre of the solar disk; observed type II or IV radio bursts; and of the position angle, width, and linear speed of observed full or partial halo CMEs. The models are designed to provide forecasts of proton fluxes with energies exceeding 10 MeV at the L1 libration point.

► The models based on a linear filter and a layer-recurrent neural network.
► Information on X-ray flares, radio bursts, and CMEs were used as input parameters.
► Forecasts of proton fluxes with energies exceeding 10 MeV at the L1 libration point.
► Practical recipes for alert announcements for critical proton fluxes were proposed.