Toward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms

Karbasi, Masoud; Jamei, Mehdi; Ahmadianfar, Iman; Asadi, Amin

doi:10.1038/s41598-021-99166-3

Cited by 15 publications

(2 citation statements)

References 75 publications

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“…Akbari et al (2019) studied the proficiency of Machine Learning (ML) and nonlinear and multilinear regression models for the Cd of PK and showed that the GPR model surpasses other ML models in predicting the Cd of PK weir. Karbasi et al (2021) showed that the GPR provided higher accuracy in predicting the side orifice discharge coefficient. The modelling results of a Cd radial gate indicated that the GPR attained acceptable predictable performance (Tao et al, 2022).…”

Section: Introductionmentioning

confidence: 97%

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

Seyedian,

Kisi

2024

Journal of Hydrology and Hydromechanics

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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.

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Section: Introductionmentioning

confidence: 97%

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

Seyedian,

Kisi

2024

Journal of Hydrology and Hydromechanics

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“…Consequently, machine learning has gained significant popularity in hydraulic engineering, often used in conjunction with other approaches like Artificial Neural Network, Support Vector Machine, and Adaptive-Network-based Fuzzy Inference Systems [27][28][29][30]. Machine learning methods offer a promising approach to predict discharge coefficients using experimental data, addressing a major challenge in hydraulic engineering research [25].…”

Section: Introductionmentioning

confidence: 99%

Hydraulic Behavior of Weir-Gate Structure with Rectangular Side Obstacle under Free and Submerged Flow Conditions

Qasim,

Dawood

2023

I2M

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This research examines the behavior of a weir-gate hydraulic structure when subjected to the presence or absence of a side obstacle. The study focuses on two scenarios, free flow and submerged flow, and employs a side obstacle with dimensions of 15 cm in height, 30 cm in length, and 1 cm in thickness, located 30 cm downstream from the weir-gate structure. A correlation matrix is derived using measured and calculated data to establish the relationship between various variables, both dimensional and non-dimensional. The results show a clear correlation between Froude number and discharge coefficient, while a random trend is observed between discharge coefficient and Reynolds number. A proper trend is found between Reynolds number and Froude number at the downstream location. The discharge coefficient is significantly influenced by factors such as gate water depth, weir water head, and vertical distance between the weir and gate. Additionally, the study investigates the effect of average water depth at the downstream on discharge and flow velocity. Two models, linear regression and artificial neural network, are developed and tested to estimate the coefficient of discharge (Cd). The ANN model proves to be the best-fit model for all scenarios, with an MSE of 0.00013141 and an R-squared value of 0.99. The machine learning algorithms used in this study demonstrate an increase in prediction accuracy for Cd.

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An efficient hybrid grey wolf optimization-based KELM approach for prediction of the discharge coefficient of submerged radial gates

2022

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Radial gates are widely used hydraulic structures for flow control in irrigation canals.Accurately prediction of discharge coefficient through radial gates is considered as a challenging hydraulic subject, particularly under highly submerged flow conditions. Incurring the advantages of Kernel-depend Extreme Learning Machine (KELM), this study offers a Grey Wolf Optimization-based KELM (GWO-KELM) for effective prediction of discharge coefficient through submerged radial gates. Additionally, Support Vector Machine (SVM), and Gaussian Process Regression (GPR) methods are also presented for comparative purposes. To build prediction models using GWO-KELM, GPR, and SVM an extensive experimental database was established, consisting of 2125 data samples gathered by the US Bureau of Reclamation. From simulation results, it is observed that the proposed GWO-KELM approach with input parameters of the ratio of the downstream flow depth to the gate opening (y3/w) and submergence ratio (y1-y3/w) provides the best performance with the correlation coefficient (R) of 0.983, the Determination Coefficient (DC) of 0.966 and the Root Mean Squared Error (RMSE) of 0.027. Furthermore, the obtained results showed that the employed kernel-depend methods are capable of a statistically predicting the discharge coefficient under varied submergence conditions with satisfactory level of accuracy.

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Toward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms

Cited by 15 publications

References 75 publications

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

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

Hydraulic Behavior of Weir-Gate Structure with Rectangular Side Obstacle under Free and Submerged Flow Conditions

An efficient hybrid grey wolf optimization-based KELM approach for prediction of the discharge coefficient of submerged radial gates

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