  This paper proposes a model based on gene expression programming for
predicting the discharge coefficient of triangular labyrinth weirs. The
parameters influencing discharge coefficient prediction were first examined and
presented as crest height ratio to the head over the crest of the weir, a crest
length of water to channel width, a crest length of water to the head over the
crest of the weir, Froude number and vertex angle dimensionless parameters.
Different models were then presented using sensitivity analysis in order to
examine each of the dimensionless parameters presented in this study. In
addition, an equation was presented through the use of nonlinear regression
(NLR) for the purpose of comparison with GEP. The results of the studies
conducted by using different statistical indexes indicated that GEP is more
capable than NLR. This is to the extent that GEP predicts the discharge
coefficient with an average relative error of approximately 2.5% in such a
manner that the predicted values have less than 5% relative error in the worst
model.

Fluid Dynamics;Machine Learning;Neural and Evolutionary Computing