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

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Authors

  • Nguyen Minh Ngoc (Corresponding Author) Hanoi Architectural University
  • Phạm Hong Cuong Vietnam Academy for Water Resources
  • Bui Hai Phong Hanoi Architectural University

DOI:

https://doi.org/10.54939/1859-1043.j.mst.82.2022.150-158

Keywords:

Machine Learning; Sequent depth; Hydraulic jump; Trapezoidal channel; Pi theory; Random Forest.

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.  

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Published

28-10-2022

How to Cite

Nguyen Minh Ngoc, Phạm Hong Cuong, and B. H. P. phong. “Prediction of the Conjugate Depth of the Hydraulic Jump in the Trapezoidal Channel Using Random Forest Regression”. Journal of Military Science and Technology, no. 82, Oct. 2022, pp. 150-8, doi:10.54939/1859-1043.j.mst.82.2022.150-158.

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