This paper presents a classification using a decision tree algorithm of hydraulic jump over rough beds based on the approach Froude number, Fr1. Specifically, 581 datasets, from literature, were analyzed. Of these, 280 datasets were for natural rough beds and 301 were for artificial rough beds. The said dataset was divided into four classes based on the energy losses. To compare the performance of the decision tree classifier (J48), a multi-layer neural network (NN) was used. The results suggest an improved performance in terms of classification accuracy by the J48 algorithm in comparison to the NN classifier. Furthermore, the classifier model had only four leaves and achieved an accuracy of 91.56%. Furthermore, classification results showed that the first class (A) of hydraulic jump over the rough beds is approximately similar to that for the smooth bed. Moreover, in the next three classes (B, C, and D), upper values of Fr1 decreased with respect to the smooth bed classes. Lastly, in class D, the upper value of Fr1 reduced to 7.45, which indicates that the shear stress (i.e., the energy loss) grows sharply with increasing Fr1. Put simply, bed roughness effectively increases the energy dissipation with an increase in the Fr1.
Experimental Characterization of the Hydraulic Jump Profile and Velocity Distribution in a Stilling Basin Physical Model