  We predict the surface density and clustering bias of H$\alpha$ emitting
galaxies for the Euclid and Nancy Grace Roman Space Telescope redshift surveys
using a new calibration of the GALFORM galaxy formation model. We generate 3000
GALFORM models to train an ensemble of deep learning algorithms to create an
emulator. We then use this emulator in a Markov Chain Monte Carlo (MCMC)
parameter search of an eleven-dimensional parameter space, to find a
best-fitting model to a calibration dataset that includes local luminosity
function data, and, for the first time, higher redshift data, namely the number
counts of H$\alpha$ emitters. We discover tensions when exploring fits for the
observational data when applying a heuristic weighting scheme in the MCMC
framework. We find improved fits to the H$\alpha$ number counts while
maintaining appropriate predictions for the local universe luminosity function.
For a flux limited Euclid-like survey to a depth of 2$\times$10$^{-16}$
erg$^{-1}$ s$^{-1}$ cm$^{-2}$ for sources in the redshift range 0.9 < $z$ <
1.8, we estimate 2962-4331 H$\alpha$ emission-line sources deg$^{-2}$. For a
Nancy Grace Roman survey, with a flux limit of 1$\times$10$^{-16}$ erg$^{-1}$
s$^{-1}$ cm$^{-2}$ and a redshift range 1.0 < $z$ < 2.0, we predict 6786-10322
H$\alpha$ emission-line sources deg$^{-2}$.
