Have a personal or library account? Click to login
Integrating machine learning and empirical approaches for scour depth estimation at sluice gates: evaluating tree-based models, hyperparameter tuning, and proposing new formulas Cover

Integrating machine learning and empirical approaches for scour depth estimation at sluice gates: evaluating tree-based models, hyperparameter tuning, and proposing new formulas

Journal of Hydrology and Hydromechanics
Volume 73 (2025): Issue 1 (March 2025)
By: Xuan-Hien Le and  Le Thi Thu Hien  
Open Access
|Mar 2025

Metrics

DOI: https://doi.org/10.2478/johh-2025-0004 | Journal eISSN: 1338-4333 | Journal ISSN: 0042-790X
Journal RSS Feed
Language: English
Page range: 51 - 64
Submitted on: Aug 28, 2024
|
Accepted on: Dec 8, 2024
|
Published on: Mar 5, 2025
Published by: Slovak Academy of Sciences, Institute of Hydrology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Extra Trees (ERT),
Histogram-Based Gradient Boosting (HGB),
Hyperparameter Tuning,
Particle Swarm Optimization (PSO),
Scour depth estimation,
Tree-structured Parzen Estimator (TPE)
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2025 Xuan-Hien Le, Le Thi Thu Hien, published by Slovak Academy of Sciences, Institute of Hydrology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Previous articleVolume 73 (2025): Issue 1 (March 2025)Next article