  The future 21 cm intensity mapping observations constitute a promising way to
trace the matter distribution of the Universe and probe cosmology. Here we
assess its capability for cosmological constraints using as a case study the
BINGO radio telescope, that will survey the Universe at low redshifts ($0.13 <
z < 0.45$). We use neural networks (NNs) to map summary statistics, namely, the
angular power spectrum (APS) and the Minkowski functionals (MFs), calculated
from simulations into cosmological parameters. Our simulations span a wide grid
of cosmologies, sampled under the $\Lambda$CDM scenario, {$\Omega_c, h$}, and
under an extension assuming the Chevallier-Polarski-Linder (CPL)
parameterization, {$\Omega_c, h, w_0, w_a$}. In general, NNs trained over APS
outperform those using MFs, while their combination provides 27% (5%) tighter
error ellipse in the $\Omega_c-h$ plane under the $\Lambda$CDM scenario (CPL
parameterization) compared to the individual use of the APS. Their combination
allows predicting $\Omega_c$ and $h$ with 4.9% and 1.6% fractional errors,
respectively, which increases to 6.4% and 3.7% under CPL parameterization.
Although we find large bias on $w_a$ estimates, we still predict $w_0$ with
24.3% error. We also confirm our results to be robust to foreground
contamination, besides finding the instrumental noise to cause the greater
impact on the predictions. Still, our results illustrate the capability of
future low redshift 21 cm observations in providing competitive cosmological
constraints using NNs, showing the ease of combining different summary
statistics.
