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Machine Learning Based Prediction of Hydraulic Jump Lengths in Stilling Basins for Barrages and Low-Head Hydraulic Structures

Authors

  • Abdul Rehman

    Civil Engineering Department, University of Engineering and Technology, Lahore, Pakistan
    Author

  • Habib ur Rehman

    Civil Engineering Department, University of Engineering and Technology, Lahore, Pakistan
    Author

  • Noor Muhammad Khan

    Civil Engineering Department, University of Engineering and Technology, Lahore, Pakistan
    Author

DOI:

https://doi.org/10.22581/0320

Keywords:

Appurtenances, Hydraulic Jump, Machine Learning, Stilling Basins, Low Froude Number

Abstract

A hydraulic-jump-type stilling basin is a popular choice for energy dissipation, where the high-velocity, supercritical flow from a dam spillway transitions into a low-velocity, subcritical flow. Stilling basins are designed such that the hydraulic jump forms within the basin, ensuring the safety of the downstream natural streambed. Thus, prediction of length of hydraulic jump is critical in deciding the basins dimensions. Experimental data on hydraulic jump type stilling basins under low inflow Froude number were collected from the literature. Six machine learning (ML) models were trained and tested using experimental data related to low inflow Froude number stilling basins. For each model, the feature importance score was calculated using a permutation-based method, which showed that the inflow Froude number (F1) and the sequent depth ratio (Y) were most significant features for prediction of the length of the hydraulic jump across all ML models. The evaluation of the models' performance indicated that the artificial neural network based model and CatBoost performed better, with higher coefficients of determination (from 0.85 to 0.93), lower error metrics, and a balanced distribution of errors during training, testing, and validation. Whereas Random Forest and AdaBoost based models showed least coefficient of determination (both having 0.84) at the model training stage. Support vector machine based model performed least at the testing stage with coefficient of determination of 0.54.

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Author Biography

  • Habib ur Rehman, Civil Engineering Department, University of Engineering and Technology, Lahore, Pakistan

    Professor Emeritus, Civil Engineering Department, UET, Lahore

    Dean, Faculty of Engineering and Technology, The University of Lahore, Lahore, Pakistan

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Published

2026-01-01

Issue

Vol. 45 No. 1 (2026): January Issue

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Articles

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Copyright (c) 2026 Mehran University Research Journal of Engineering and Technology

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How to Cite

Machine Learning Based Prediction of Hydraulic Jump Lengths in Stilling Basins for Barrages and Low-Head Hydraulic Structures. (2026). Mehran University Research Journal of Engineering and Technology, 45(1), 47-61. https://doi.org/10.22581/0320

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