Insights into the prediction capability of roughness coefficient in current ripple bedforms under varied hydraulic conditions

Ubiquitous flow bedforms such as ripples in rivers and coastal environments can affect transport conditions as they constitute the bed roughness elements. The roughness coefficient needs to be adequately quantified owing to its significant influence on the performance of hydraulic structures and riv...

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Autores principales:	Kiyoumars Roushangar, Saman Shahnazi
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	artificial neural network (ann) gaussian process regression (gpr) manning's coefficient ripple bedforms support vector machine (svm) Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/c88494354dfb4b4e992f810eeafde19d
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spelling	oai:doaj.org-article:c88494354dfb4b4e992f810eeafde19d2021-11-23T18:48:34ZInsights into the prediction capability of roughness coefficient in current ripple bedforms under varied hydraulic conditions1464-71411465-173410.2166/hydro.2021.161https://doaj.org/article/c88494354dfb4b4e992f810eeafde19d2021-11-01T00:00:00Zhttp://jh.iwaponline.com/content/23/6/1182https://doaj.org/toc/1464-7141https://doaj.org/toc/1465-1734Ubiquitous flow bedforms such as ripples in rivers and coastal environments can affect transport conditions as they constitute the bed roughness elements. The roughness coefficient needs to be adequately quantified owing to its significant influence on the performance of hydraulic structures and river management. This work intended to evaluate the sensitivity and robustness of three machine learning (ML) methods, namely, Gaussian process regression (GPR), artificial neural network (ANN), and support vector machine (SVM) for the prediction of the Manning's roughness coefficient of channels with ripple bedforms. To this end, 840 experimental data points considering various hydraulic conditions were prepared. According to the obtained results, GPR was found to accurately predict the Manning's coefficient with input parameters of Reynolds number (Re), depth to width ratio (y/b), the ratio of the hydraulic radius to the median grain diameter (R/D50), and grain Froude number (). Moreover, sensitivity analysis was implemented with proposed ML approaches which indicated that the ratio of the hydraulic radius to the median grain diameter has a considerable role in modeling the Manning's coefficient in channels with ripple bedforms. HIGHLIGHTS GPR, SVM and ANN were selected to identify influential parameters for prediction of roughness coefficient of ripple bedforms.; 840 experimental data points from different sources were used to feed the utilized models.; Prediction capability of roughness coefficient was investigated under varied hydraulic conditions.;Kiyoumars RoushangarSaman ShahnaziIWA Publishingarticleartificial neural network (ann)gaussian process regression (gpr)manning's coefficientripple bedformssupport vector machine (svm)Information technologyT58.5-58.64Environmental technology. Sanitary engineeringTD1-1066ENJournal of Hydroinformatics, Vol 23, Iss 6, Pp 1182-1196 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial neural network (ann) gaussian process regression (gpr) manning's coefficient ripple bedforms support vector machine (svm) Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
spellingShingle	artificial neural network (ann) gaussian process regression (gpr) manning's coefficient ripple bedforms support vector machine (svm) Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066 Kiyoumars Roushangar Saman Shahnazi Insights into the prediction capability of roughness coefficient in current ripple bedforms under varied hydraulic conditions
description	Ubiquitous flow bedforms such as ripples in rivers and coastal environments can affect transport conditions as they constitute the bed roughness elements. The roughness coefficient needs to be adequately quantified owing to its significant influence on the performance of hydraulic structures and river management. This work intended to evaluate the sensitivity and robustness of three machine learning (ML) methods, namely, Gaussian process regression (GPR), artificial neural network (ANN), and support vector machine (SVM) for the prediction of the Manning's roughness coefficient of channels with ripple bedforms. To this end, 840 experimental data points considering various hydraulic conditions were prepared. According to the obtained results, GPR was found to accurately predict the Manning's coefficient with input parameters of Reynolds number (Re), depth to width ratio (y/b), the ratio of the hydraulic radius to the median grain diameter (R/D50), and grain Froude number (). Moreover, sensitivity analysis was implemented with proposed ML approaches which indicated that the ratio of the hydraulic radius to the median grain diameter has a considerable role in modeling the Manning's coefficient in channels with ripple bedforms. HIGHLIGHTS GPR, SVM and ANN were selected to identify influential parameters for prediction of roughness coefficient of ripple bedforms.; 840 experimental data points from different sources were used to feed the utilized models.; Prediction capability of roughness coefficient was investigated under varied hydraulic conditions.;
format	article
author	Kiyoumars Roushangar Saman Shahnazi
author_facet	Kiyoumars Roushangar Saman Shahnazi
author_sort	Kiyoumars Roushangar
title	Insights into the prediction capability of roughness coefficient in current ripple bedforms under varied hydraulic conditions
title_short	Insights into the prediction capability of roughness coefficient in current ripple bedforms under varied hydraulic conditions
title_full	Insights into the prediction capability of roughness coefficient in current ripple bedforms under varied hydraulic conditions
title_fullStr	Insights into the prediction capability of roughness coefficient in current ripple bedforms under varied hydraulic conditions
title_full_unstemmed	Insights into the prediction capability of roughness coefficient in current ripple bedforms under varied hydraulic conditions
title_sort	insights into the prediction capability of roughness coefficient in current ripple bedforms under varied hydraulic conditions
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/c88494354dfb4b4e992f810eeafde19d
work_keys_str_mv	AT kiyoumarsroushangar insightsintothepredictioncapabilityofroughnesscoefficientincurrentripplebedformsundervariedhydraulicconditions AT samanshahnazi insightsintothepredictioncapabilityofroughnesscoefficientincurrentripplebedformsundervariedhydraulicconditions
_version_	1718416180968947712

Insights into the prediction capability of roughness coefficient in current ripple bedforms under varied hydraulic conditions

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