  The forthcoming Dark Energy Spectroscopic Instrument (DESI) experiment plans
to measure the effects of dark energy on the expansion of the Universe and
create a $3$D map of the Universe using galaxies up to $z \sim 1.6$ and QSOs up
to $z \sim 3.5$. In order to create this map, DESI will obtain spectroscopic
redshifts of over $30$ million objects; among them, a majority are \oii
emitting star-forming galaxies known as emission-line galaxies (ELGs). These
ELG targets will be pre-selected by drawing a selection region on the $g - r$
vs. $r - z$ colour-colour plot, where high redshift ELGs form a separate locus
from the lower redshift ELGs and interlopers. In this paper, we study the
efficiency of three ELG target selection algorithms -- the final design report
(FDR) cut based on the DEEP2 photometry, Number Density Modelling and Random
Forest -- to determine how the combination of these three algorithms can be
best used to yield a simple selection boundary that will be best suited to meet
DESI's science goals. To do this, we selected $17$ small patches in the DESI
footprint where we run the three target selection algorithms to pre-select ELGs
based on their photometry. We observed the pre-selected ELGs using the MMT
Binospec, which is similar in functionality to the DESI instrument, to obtain
their spectroscopic redshifts and fluxes of $1054$ ELGs. By analysing the
redshift and fluxing distribution of these galaxies, we find that although NDM
performed the best, simple changes in the FDR definition would also yield
sufficient performance.
