Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods

The accurate prediction of the mean wave overtopping rate at breakwaters is vital for a safe design. Hence, providing a robust tool as a preliminary estimator can be useful for practitioners. Recently, soft computing tools such as artificial neural networks (ANN) have been developed as alternatives...

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Autores principales:	Shabnam Hosseinzadeh, Amir Etemad-Shahidi, Ali Koosheh
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	ard-mattern5/2-gaussian process regression (gpr) ffbp-artificial neural network (ann) kernel-based models mean wave overtopping rbf-support vector regression (svr) simple sloped breakwaters Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/afcfc04513604982a298bbaa3f70596c
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spelling	oai:doaj.org-article:afcfc04513604982a298bbaa3f70596c2021-11-05T17:51:23ZPrediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods1464-71411465-173410.2166/hydro.2021.046https://doaj.org/article/afcfc04513604982a298bbaa3f70596c2021-09-01T00:00:00Zhttp://jh.iwaponline.com/content/23/5/1030https://doaj.org/toc/1464-7141https://doaj.org/toc/1465-1734The accurate prediction of the mean wave overtopping rate at breakwaters is vital for a safe design. Hence, providing a robust tool as a preliminary estimator can be useful for practitioners. Recently, soft computing tools such as artificial neural networks (ANN) have been developed as alternatives to traditional overtopping formulae. The goal of this paper is to assess the capabilities of two kernel-based methods, namely Gaussian process regression (GPR) and support vector regression for the prediction of mean wave overtopping rate at sloped breakwaters. An extensive dataset taken from the EurOtop database, including rubble mound structures with permeable core, straight slopes, without berm, and crown wall, was employed to develop the models. Different combinations of the important dimensionless parameters representing structural features and wave conditions were tested based on the sensitivity analysis for developing the models. The obtained results were compared with those of the ANN model and the existing empirical formulae. The modified Taylor diagram was used to compare the models graphically. The results showed the superiority of kernel-based models, especially the GPR model over the ANN model and empirical formulae. In addition, the optimal input combination was introduced based on accuracy and the number of input parameters criteria. Finally, the physical consistencies of developed models were investigated, the results of which demonstrated the reliability of kernel-based models in terms of delivering physics of overtopping phenomenon. HIGHLIGHTS Gaussian process regression (GPR) and support vector regression (SVR) methods were employed to predict the mean wave overtopping rate at simple sloped breakwaters.; The performances of GPR and SVR models were compared with those of ANN model and existing empirical formulae.; GPR and SVR models showed better performances compared to those of the ANN model and empirical formulae.; The optimal input combination with fewer number of input parameters, extracted from sensitivity analysis, and high accuracy was introduced.; Physical consistency of developed GPR and SVR models were investigated based on the observed trend between the most effective input parameter and mean wave overtopping rate.;Shabnam HosseinzadehAmir Etemad-ShahidiAli KooshehIWA Publishingarticleard-mattern5/2-gaussian process regression (gpr)ffbp-artificial neural network (ann)kernel-based modelsmean wave overtoppingrbf-support vector regression (svr)simple sloped breakwatersInformation technologyT58.5-58.64Environmental technology. Sanitary engineeringTD1-1066ENJournal of Hydroinformatics, Vol 23, Iss 5, Pp 1030-1049 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ard-mattern5/2-gaussian process regression (gpr) ffbp-artificial neural network (ann) kernel-based models mean wave overtopping rbf-support vector regression (svr) simple sloped breakwaters Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
spellingShingle	ard-mattern5/2-gaussian process regression (gpr) ffbp-artificial neural network (ann) kernel-based models mean wave overtopping rbf-support vector regression (svr) simple sloped breakwaters Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066 Shabnam Hosseinzadeh Amir Etemad-Shahidi Ali Koosheh Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods
description	The accurate prediction of the mean wave overtopping rate at breakwaters is vital for a safe design. Hence, providing a robust tool as a preliminary estimator can be useful for practitioners. Recently, soft computing tools such as artificial neural networks (ANN) have been developed as alternatives to traditional overtopping formulae. The goal of this paper is to assess the capabilities of two kernel-based methods, namely Gaussian process regression (GPR) and support vector regression for the prediction of mean wave overtopping rate at sloped breakwaters. An extensive dataset taken from the EurOtop database, including rubble mound structures with permeable core, straight slopes, without berm, and crown wall, was employed to develop the models. Different combinations of the important dimensionless parameters representing structural features and wave conditions were tested based on the sensitivity analysis for developing the models. The obtained results were compared with those of the ANN model and the existing empirical formulae. The modified Taylor diagram was used to compare the models graphically. The results showed the superiority of kernel-based models, especially the GPR model over the ANN model and empirical formulae. In addition, the optimal input combination was introduced based on accuracy and the number of input parameters criteria. Finally, the physical consistencies of developed models were investigated, the results of which demonstrated the reliability of kernel-based models in terms of delivering physics of overtopping phenomenon. HIGHLIGHTS Gaussian process regression (GPR) and support vector regression (SVR) methods were employed to predict the mean wave overtopping rate at simple sloped breakwaters.; The performances of GPR and SVR models were compared with those of ANN model and existing empirical formulae.; GPR and SVR models showed better performances compared to those of the ANN model and empirical formulae.; The optimal input combination with fewer number of input parameters, extracted from sensitivity analysis, and high accuracy was introduced.; Physical consistency of developed GPR and SVR models were investigated based on the observed trend between the most effective input parameter and mean wave overtopping rate.;
format	article
author	Shabnam Hosseinzadeh Amir Etemad-Shahidi Ali Koosheh
author_facet	Shabnam Hosseinzadeh Amir Etemad-Shahidi Ali Koosheh
author_sort	Shabnam Hosseinzadeh
title	Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods
title_short	Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods
title_full	Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods
title_fullStr	Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods
title_full_unstemmed	Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods
title_sort	prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/afcfc04513604982a298bbaa3f70596c
work_keys_str_mv	AT shabnamhosseinzadeh predictionofmeanwaveovertoppingatsimpleslopedbreakwatersusingkernelbasedmethods AT amiretemadshahidi predictionofmeanwaveovertoppingatsimpleslopedbreakwatersusingkernelbasedmethods AT alikoosheh predictionofmeanwaveovertoppingatsimpleslopedbreakwatersusingkernelbasedmethods
_version_	1718444119750082560

Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods

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