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

Abstract In the present study, two kernel-based data-intelligence paradigms, namely, Gaussian Process Regression (GPR) and Kernel Extreme Learning Machine (KELM) along with Generalized Regression Neural Network (GRNN) and Response Surface Methodology (RSM), as the validated schemes, employed to prec...

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Autores principales:	Masoud Karbasi, Mehdi Jamei, Iman Ahmadianfar, Amin Asadi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2ff497319f394f2094fc041fb8ac6c95
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spelling	oai:doaj.org-article:2ff497319f394f2094fc041fb8ac6c952021-12-02T18:37:11ZToward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms10.1038/s41598-021-99166-32045-2322https://doaj.org/article/2ff497319f394f2094fc041fb8ac6c952021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99166-3https://doaj.org/toc/2045-2322Abstract In the present study, two kernel-based data-intelligence paradigms, namely, Gaussian Process Regression (GPR) and Kernel Extreme Learning Machine (KELM) along with Generalized Regression Neural Network (GRNN) and Response Surface Methodology (RSM), as the validated schemes, employed to precisely estimate the elliptical side orifice discharge coefficient in rectangular channels. A total of 588 laboratory data in various geometric and hydraulic conditions were used to develop the models. The discharge coefficient was considered as a function of five dimensionless hydraulically and geometrical variables. The results showed that the machine learning models used in this study had shown good performance compared to the regression-based relationships. Comparison between machine learning models showed that GPR (RMSE = 0.0081, R = 0.958, MAPE = 1.3242) and KELM (RMSE = 0.0082, R = 0.9564, MAPE = 1.3499) models provide higher accuracy. Base on the RSM model, a new practical equation was developed to predict the discharge coefficient. Also, the sensitivity analysis of the input parameters showed that the main channel width to orifice height ratio (B/b) has the most significant effect on determining the discharge coefficient. The leveraged approach was applied to identify outlier data and applicability domain.Masoud KarbasiMehdi JameiIman AhmadianfarAmin AsadiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Masoud Karbasi Mehdi Jamei Iman Ahmadianfar Amin Asadi Toward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms
description	Abstract In the present study, two kernel-based data-intelligence paradigms, namely, Gaussian Process Regression (GPR) and Kernel Extreme Learning Machine (KELM) along with Generalized Regression Neural Network (GRNN) and Response Surface Methodology (RSM), as the validated schemes, employed to precisely estimate the elliptical side orifice discharge coefficient in rectangular channels. A total of 588 laboratory data in various geometric and hydraulic conditions were used to develop the models. The discharge coefficient was considered as a function of five dimensionless hydraulically and geometrical variables. The results showed that the machine learning models used in this study had shown good performance compared to the regression-based relationships. Comparison between machine learning models showed that GPR (RMSE = 0.0081, R = 0.958, MAPE = 1.3242) and KELM (RMSE = 0.0082, R = 0.9564, MAPE = 1.3499) models provide higher accuracy. Base on the RSM model, a new practical equation was developed to predict the discharge coefficient. Also, the sensitivity analysis of the input parameters showed that the main channel width to orifice height ratio (B/b) has the most significant effect on determining the discharge coefficient. The leveraged approach was applied to identify outlier data and applicability domain.
format	article
author	Masoud Karbasi Mehdi Jamei Iman Ahmadianfar Amin Asadi
author_facet	Masoud Karbasi Mehdi Jamei Iman Ahmadianfar Amin Asadi
author_sort	Masoud Karbasi
title	Toward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms
title_short	Toward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms
title_full	Toward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms
title_fullStr	Toward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms
title_full_unstemmed	Toward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms
title_sort	toward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2ff497319f394f2094fc041fb8ac6c95
work_keys_str_mv	AT masoudkarbasi towardtheaccurateestimationofellipticalsideorificedischargecoefficientapplyingtworigorouskernelbaseddataintelligenceparadigms AT mehdijamei towardtheaccurateestimationofellipticalsideorificedischargecoefficientapplyingtworigorouskernelbaseddataintelligenceparadigms AT imanahmadianfar towardtheaccurateestimationofellipticalsideorificedischargecoefficientapplyingtworigorouskernelbaseddataintelligenceparadigms AT aminasadi towardtheaccurateestimationofellipticalsideorificedischargecoefficientapplyingtworigorouskernelbaseddataintelligenceparadigms
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Toward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms

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