Impact of downstream obstructions on ogee weir efficiency: a regression analysis

This study delves into the impact of downstream obstruction angles on the discharge coefficient (Cd) over ogee weirs within open channel flows, a critical factor for accurate flow rate predictions in hydraulic engineering. Employing a series of detailed laboratory experiments, the influence of vario...

Full description

Saved in:
Bibliographic Details
Published in:Journal of hydroinformatics 2024-05, Vol.26 (5), p.1217-1233
Main Authors: S. M., Shravan Kumar, Patil, Chidanand, Yadav, Anamika, Bukke, Lavanya, Reddy, R. Laxmana, Sakare, Praveen Kumar
Format: Article
Language:English
Subjects:
Accuracy
Angles (geometry)
Bayesian analysis
Cavitation
Channel flow
Decision trees
Discharge coefficient
downstream obstruction angles
Efficiency
Flow rates
Flow velocity
Geographic information systems
Hydraulic engineering
Integrated approach
Investigations
Laboratory experimentation
Machine learning
Mathematical models
Obstructions
ogee weir
Open channel flow
Open channels
Performance prediction
Prediction models
Probability theory
Regression analysis
Regression models
Remote sensing
Root-mean-square errors
Spatial data
Support vector machines
Water levels
Weirs
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This study delves into the impact of downstream obstruction angles on the discharge coefficient (Cd) over ogee weirs within open channel flows, a critical factor for accurate flow rate predictions in hydraulic engineering. Employing a series of detailed laboratory experiments, the influence of various obstruction angles on Cd was scrutinized applying a suite of regression analysis to develop predictive models. The analysis was enriched by considering hydraulic parameters such as flow rate, water level, and weir geometry. Despite the established importance of Cd in hydraulic designs the nuanced effects of downstream obstructions have received limited attention, highlighting a critical research gap. The findings highlight a strong correlation between obstruction angles and Cd, with developed regression models demonstrating notable predictive strength. Remarkably the models exhibited varying levels of accuracy, with the Random Forest regressor achieving an exceptionally low root mean square error (RMSE) of 0.005, indicating superior predictive performance. Conversely, traditional models like Decision tree and XG BOOST reflected higher RMSE values of 0.60, suggesting less predictive accuracy in this context. LASSO, Bayesian Ridge, and OMP regressors stood out with an RMSE of zero, denoting perfect predictions under the study's specific conditions.
ISSN:1464-7141
1465-1734
DOI:10.2166/hydro.2024.029