  This paper presents several approaches to deal with the problem of
identifying muons in a water Cherenkov detector with a reduced water volume and
4 PMTs. Different perspectives of information representation are used and new
features are engineered using the specific domain knowledge. As results show,
these new features, in combination with the convolutional layers, are able to
achieve a good performance avoiding overfitting and being able to generalise
properly for the test set. The results also prove that the combination of
state-of-the-art Machine Learning analysis techniques and water Cherenkov
detectors with low water depth can be used to efficiently identify muons, which
may lead to huge investment savings due to the reduction of the amount of water
needed at high altitudes. This achievement can be used in further research to
be able to discriminate between gamma and hadron induced showers using muons as
discriminant.
