Prediction of the conjugate depth of the hydraulic jump in the trapezoidal channel using Random Forest regression

Abstract

Prediction of the sequent depths of the hydraulic jump in the trapezoidal channel using the theoretical equation is a challenging task. Therefore, existing studies have attempted to solve the task by conducting experiments or using semi-empirical calculations. The paper proposes a novel method that applies Buckingham's Pi theory and the Random Forest regression to improve the prediction accuracy of the sequent depths of the hydraulic jump in the trapezoidal channel. The study has shown that Machine Learning models can be efficient for the determination of the geometrical features of the jump and have high ability in many real projects.