  One of the most important subjects of hydraulic engineering is the reliable
estimation of the transverse distribution in the rectangular channel of bed and
wall shear stresses. This study makes use of the Tsallis entropy, genetic
programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) methods to
assess the shear stress distribution (SSD) in the rectangular channel. To
evaluate the results of the Tsallis entropy, GP and ANFIS models, laboratory
observations were used in which shear stress was measured using an optimized
Preston tube. This is then used to measure the SSD in various aspect ratios in
the rectangular channel. To investigate the shear stress percentage, 10 data
series with a total of 112 different data were used. The results of the
sensitivity analysis show that the most influential parameter for the SSD in a
smooth rectangular channel is the dimensionless parameter B/H, Where the
transverse coordinate is B, and the flow depth is H. With the parameters (b/B),
(B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the
GP was better than the other one. Based on the analysis, it can be concluded
that the use of GP and ANFIS algorithms is more effective in estimating shear
stress in smooth rectangular channels than the Tsallis entropy-based equations.
