<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The present study used three machine learning models, including Least Square Support Vector Regression (LSSVR) and two non-parametric models, namely, Quantile Regression Forest (QRF) and Gaussian Process Regression (GPR), to quantify uncertainty and precisely predict the side weir discharge coefficient (Cd) in rectangular channels. So, 15 input structures were examined to develop the models. The results revealed that the machine learning models used in the study offered better accuracy compared to the classical equations. While the LSSVR and QRF models provided a good prediction performance, the GPR slightly outperformed them. The best input structure that was developed included all four dimensionless parameters. Sensitivity analysis was conducted to identify the effective parameters. To evaluate the uncertainty in the predictions, the LSSVR, QRF, and GPR were used to generate prediction intervals (PI), which quantify the uncertainty coupled with point prediction. Among the implemented models, the GPR and LSSVR models provided more reliable results based on PI width and the percentage of observed data covered by PI. According to point prediction and uncertainty analysis, it was concluded that the GPR model had a lower uncertainty and could be successfully used to predict Cd.</p>
</abstract>

The present study used three machine learning models, including Least Square Support Vector Regression (LSSVR) and two non-parametric models, namely, Quantile Regression Forest (QRF) and Gaussian Process Regression (GPR), to quantify uncertainty and precisely predict the side weir discharge coefficient (Cd) in rectangular channels. So, 15 input structures were examined to develop the models. The results revealed that the machine learning models used in the study offered better accuracy compared to the classical equations. While the LSSVR and QRF models provided a good prediction performance, the GPR slightly outperformed them. The best input structure that was developed included all four dimensionless parameters. Sensitivity analysis was conducted to identify the effective parameters. To evaluate the uncertainty in the predictions, the LSSVR, QRF, and GPR were used to generate prediction intervals (PI), which quantify the uncertainty coupled with point prediction. Among the implemented models, the GPR and LSSVR models provided more reliable results based on PI width and the percentage of observed data covered by PI. According to point prediction and uncertainty analysis, it was concluded that the GPR model had a lower uncertainty and could be successfully used to predict Cd.

Uncertainty analysis of discharge coefficient predicted for rectangular side weir using machine learning methods

Department of Range and Watershed Management

Journal of Hydrology and Hydromechanics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The present study used three machine learning models, including Least Square Support Vector Regression (LSSVR) and two non-parametric models, namely, Quantile...

Uncertainty analysis of discharge coefficient predicted for rectangular side weir using machine learning methods

Publié en ligne: 08 févr. 2024

Pages: 113 - 130

Reçu: 06 mars 2023

Accepté: 15 mai 2023

DOI: https://doi.org/10.2478/johh-2023-0043

Mots clés
Machine learning, Prediction intervals, Sensitivity analysis, Side weir discharge coefficient, Uncertainty analysis

© 2023 Seyed Morteza Seyedian et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Uncertainty analysis of discharge coefficient predicted for rectangular side weir using machine learning methods

Publié en ligne: 08 févr. 2024

Pages: 113 - 130

Reçu: 06 mars 2023

Accepté: 15 mai 2023

DOI: https://doi.org/10.2478/johh-2023-0043

Mots clésMachine learning, Prediction intervals, Sensitivity analysis, Side weir discharge coefficient, Uncertainty analysis

© 2023 Seyed Morteza Seyedian et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Machine learning, Prediction intervals, Sensitivity analysis, Side weir discharge coefficient, Uncertainty analysis