  Shear stress distribution prediction in open channels is of utmost importance
in hydraulic structural engineering as it directly affects the design of stable
channels. In this study, at first, a series of experimental tests were
conducted to assess the shear stress distribution in prismatic compound
channels. The shear stress values around the whole wetted perimeter were
measured in the compound channel with different floodplain widths also in
different flow depths in subcritical and supercritical conditions. A set of,
data mining and machine learning models including Random Forest (RF), M5P,
Random Committee (RC), KStar and Additive Regression Model (AR) implemented on
attained data to predict the shear stress distribution in the compound channel.
Results indicated among these five models, RF method indicated the most precise
results with the highest R2 value of 0.9. Finally, the most powerful data
mining method which studied in this research (RF) compared with two well-known
analytical models of Shiono and Knight Method (SKM) and Shannon method to
acquire the proposed model functioning in predicting the shear stress
distribution. The results showed that the RF model has the best prediction
performance compared to SKM and Shannon models.
