首页|Findings from Gonbad Kavous University Provide New Insights into Machine Learning (Uncertainty analysis of discharge coefficient predicted for rectangular side weir using machine learning methods)
Findings from Gonbad Kavous University Provide New Insights into Machine Learning (Uncertainty analysis of discharge coefficient predicted for rectangular side weir using machine learning methods)
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Investigators publish new report on artificial intelligence. According to news reporting out of Gonbad Kavous, Iran, by NewsRx editors, research stated, “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.” The news journalists obtained a quote from the research from Gonbad Kavous University: “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.”
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