  Energetic electrons inside Earth's outer Van Allen belt pose a major
radiation threat to space-borne electronics that often play vital roles in our
modern society. Ultra-relativistic electrons with energies greater than or
equal to two Megaelectron-volt (MeV) are of particular interest due to their
high penetrating ability, and thus forecasting these >=2 MeV electron levels
has significant meaning to all space sectors. Here we update the latest
development of the predictive model for MeV electrons inside the Earth's outer
radiation belt. The new version, called PreMevE-2E, focuses on forecasting
ultra-relativistic electron flux distributions across the outer radiation belt,
with no need of in-situ measurements except for at the geosynchronous (GEO)
orbit. Model inputs include precipitating electrons observed in
low-Earth-orbits by NOAA satellites, upstream solar wind conditions (speeds and
densities) from solar wind monitors, as well as ultra-relativistic electrons
measured by one Los Alamos GEO satellite. We evaluated a total of 32 supervised
machine learning models that fall into four different classes of linear and
neural network architectures, and also successfully tested ensemble forecasting
by using groups of top-performing models. All models are individually trained,
validated, and tested by in-situ electron data from NASA's Van Allen Probes
mission. It is shown that the final ensemble model generally outperforms
individual models overs L-shells, and this PreMevE-2E model provides reliable
and high-fidelity 25-hr (~1-day) and 50-hr (~2-day) forecasts with high mean
performance efficiency values. Our results also suggest this new model is
dominated by non-linear components at low L-shells (< ~4) for
ultra-relativistic electrons, which is different from the dominance of linear
components at all L-shells for 1 MeV electrons as previously discovered.
