The potential of FFNN and MLP-FFA approaches in prediction of Manning coefficient in ripple and dune bedforms

An accurate prediction of roughness coefficient is of substantial importance for river management. The current study applies two artificial intelligence methods namely; Feed-Forward Neural Network (FFNN) and Multilayer Perceptron Firefly Algorithm (MLP-FFA) to predict the Manning roughness coefficie...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Vahid Abdi, Seyed Mahdi Saghebian
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	alluvial channel bedforms flow resistance intelligence methods multilayer perceptron firefly algorithm Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/18c5de7e01ef4393bc3c02fa6fe3a656
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:18c5de7e01ef4393bc3c02fa6fe3a656
record_format	dspace
spelling	oai:doaj.org-article:18c5de7e01ef4393bc3c02fa6fe3a6562021-11-23T18:57:03ZThe potential of FFNN and MLP-FFA approaches in prediction of Manning coefficient in ripple and dune bedforms1606-97491607-079810.2166/ws.2021.150https://doaj.org/article/18c5de7e01ef4393bc3c02fa6fe3a6562021-11-01T00:00:00Zhttp://ws.iwaponline.com/content/21/7/3900https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798An accurate prediction of roughness coefficient is of substantial importance for river management. The current study applies two artificial intelligence methods namely; Feed-Forward Neural Network (FFNN) and Multilayer Perceptron Firefly Algorithm (MLP-FFA) to predict the Manning roughness coefficient in channels with dune and ripple bedforms. In this regard, based on the flow and sediment particles properties various models were developed and tested using some available experimental data sets. The obtained results showed that the applied methods had high efficiency in the Manning coefficient modeling. It was found that both flow and sediment properties were effective in modeling process. Sensitivity analysis proved that the Reynolds number plays a key role in the modeling of channel resistance with dune bedform and Froude number and the ratio of the hydraulic radius to the median grain diameter play key roles in the modeling of channel resistance with ripple bedform. Furthermore, for assessing the best-applied model dependability, uncertainty analysis was performed and obtained results showed an allowable degree of uncertainty for the MLP-FFA model in roughness coefficient modeling. HIGHLIGHTS FFNN and MLP-FFA methods were selected to identify influential parameters for prediction of roughness coefficient in alluvial channel.; Experimental datasets were used to feed the utilized models.; Uncertainty analysis was performed to evaluate the best-applied model dependability.; Results showed desirable performance of the applied models in roughness coefficient modeling.;Vahid AbdiSeyed Mahdi SaghebianIWA Publishingarticlealluvial channelbedformsflow resistanceintelligence methodsmultilayer perceptron firefly algorithmWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 7, Pp 3900-3912 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	alluvial channel bedforms flow resistance intelligence methods multilayer perceptron firefly algorithm Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	alluvial channel bedforms flow resistance intelligence methods multilayer perceptron firefly algorithm Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Vahid Abdi Seyed Mahdi Saghebian The potential of FFNN and MLP-FFA approaches in prediction of Manning coefficient in ripple and dune bedforms
description	An accurate prediction of roughness coefficient is of substantial importance for river management. The current study applies two artificial intelligence methods namely; Feed-Forward Neural Network (FFNN) and Multilayer Perceptron Firefly Algorithm (MLP-FFA) to predict the Manning roughness coefficient in channels with dune and ripple bedforms. In this regard, based on the flow and sediment particles properties various models were developed and tested using some available experimental data sets. The obtained results showed that the applied methods had high efficiency in the Manning coefficient modeling. It was found that both flow and sediment properties were effective in modeling process. Sensitivity analysis proved that the Reynolds number plays a key role in the modeling of channel resistance with dune bedform and Froude number and the ratio of the hydraulic radius to the median grain diameter play key roles in the modeling of channel resistance with ripple bedform. Furthermore, for assessing the best-applied model dependability, uncertainty analysis was performed and obtained results showed an allowable degree of uncertainty for the MLP-FFA model in roughness coefficient modeling. HIGHLIGHTS FFNN and MLP-FFA methods were selected to identify influential parameters for prediction of roughness coefficient in alluvial channel.; Experimental datasets were used to feed the utilized models.; Uncertainty analysis was performed to evaluate the best-applied model dependability.; Results showed desirable performance of the applied models in roughness coefficient modeling.;
format	article
author	Vahid Abdi Seyed Mahdi Saghebian
author_facet	Vahid Abdi Seyed Mahdi Saghebian
author_sort	Vahid Abdi
title	The potential of FFNN and MLP-FFA approaches in prediction of Manning coefficient in ripple and dune bedforms
title_short	The potential of FFNN and MLP-FFA approaches in prediction of Manning coefficient in ripple and dune bedforms
title_full	The potential of FFNN and MLP-FFA approaches in prediction of Manning coefficient in ripple and dune bedforms
title_fullStr	The potential of FFNN and MLP-FFA approaches in prediction of Manning coefficient in ripple and dune bedforms
title_full_unstemmed	The potential of FFNN and MLP-FFA approaches in prediction of Manning coefficient in ripple and dune bedforms
title_sort	potential of ffnn and mlp-ffa approaches in prediction of manning coefficient in ripple and dune bedforms
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/18c5de7e01ef4393bc3c02fa6fe3a656
work_keys_str_mv	AT vahidabdi thepotentialofffnnandmlpffaapproachesinpredictionofmanningcoefficientinrippleanddunebedforms AT seyedmahdisaghebian thepotentialofffnnandmlpffaapproachesinpredictionofmanningcoefficientinrippleanddunebedforms AT vahidabdi potentialofffnnandmlpffaapproachesinpredictionofmanningcoefficientinrippleanddunebedforms AT seyedmahdisaghebian potentialofffnnandmlpffaapproachesinpredictionofmanningcoefficientinrippleanddunebedforms
_version_	1718416137864085504

The potential of FFNN and MLP-FFA approaches in prediction of Manning coefficient in ripple and dune bedforms

Ejemplares similares