Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms.

To reduce the problem of sedimentation in open channels, calculating flow velocity is critical. Undesirable operating costs arise due to sedimentation problems. To overcome these problems, the development of machine learning based models may provide reliable results. Recently, numerous studies have...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Enes Gul, Mir Jafar Sadegh Safari, Ali Torabi Haghighi, Ali Danandeh Mehr
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/086b29e7ed3a4fdcb89f2a40998fa411
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:086b29e7ed3a4fdcb89f2a40998fa411
record_format	dspace
spelling	oai:doaj.org-article:086b29e7ed3a4fdcb89f2a40998fa4112021-12-02T20:17:10ZSediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms.1932-620310.1371/journal.pone.0258125https://doaj.org/article/086b29e7ed3a4fdcb89f2a40998fa4112021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258125https://doaj.org/toc/1932-6203To reduce the problem of sedimentation in open channels, calculating flow velocity is critical. Undesirable operating costs arise due to sedimentation problems. To overcome these problems, the development of machine learning based models may provide reliable results. Recently, numerous studies have been conducted to model sediment transport in non-deposition condition however, the main deficiency of the existing studies is utilization of a limited range of data in model development. To tackle this drawback, six data sets with wide ranges of pipe size, volumetric sediment concentration, channel bed slope, sediment size and flow depth are used for the model development in this study. Moreover, two tree-based algorithms, namely M5 rule tree (M5RT) and M5 regression tree (M5RGT) are implemented, and results are compared to the traditional regression equations available in the literature. The results show that machine learning approaches outperform traditional regression models. The tree-based algorithms, M5RT and M5RGT, provided satisfactory results in contrast to their regression-based alternatives with RMSE = 1.184 and RMSE = 1.071, respectively. In order to recommend a practical solution, the tree structure algorithms are supplied to compute sediment transport in an open channel flow.Enes GulMir Jafar Sadegh SafariAli Torabi HaghighiAli Danandeh MehrPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258125 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Enes Gul Mir Jafar Sadegh Safari Ali Torabi Haghighi Ali Danandeh Mehr Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms.
description	To reduce the problem of sedimentation in open channels, calculating flow velocity is critical. Undesirable operating costs arise due to sedimentation problems. To overcome these problems, the development of machine learning based models may provide reliable results. Recently, numerous studies have been conducted to model sediment transport in non-deposition condition however, the main deficiency of the existing studies is utilization of a limited range of data in model development. To tackle this drawback, six data sets with wide ranges of pipe size, volumetric sediment concentration, channel bed slope, sediment size and flow depth are used for the model development in this study. Moreover, two tree-based algorithms, namely M5 rule tree (M5RT) and M5 regression tree (M5RGT) are implemented, and results are compared to the traditional regression equations available in the literature. The results show that machine learning approaches outperform traditional regression models. The tree-based algorithms, M5RT and M5RGT, provided satisfactory results in contrast to their regression-based alternatives with RMSE = 1.184 and RMSE = 1.071, respectively. In order to recommend a practical solution, the tree structure algorithms are supplied to compute sediment transport in an open channel flow.
format	article
author	Enes Gul Mir Jafar Sadegh Safari Ali Torabi Haghighi Ali Danandeh Mehr
author_facet	Enes Gul Mir Jafar Sadegh Safari Ali Torabi Haghighi Ali Danandeh Mehr
author_sort	Enes Gul
title	Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms.
title_short	Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms.
title_full	Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms.
title_fullStr	Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms.
title_full_unstemmed	Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms.
title_sort	sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/086b29e7ed3a4fdcb89f2a40998fa411
work_keys_str_mv	AT enesgul sedimenttransportmodelinginnondepositionwithcleanbedconditionusingdifferenttreebasedalgorithms AT mirjafarsadeghsafari sedimenttransportmodelinginnondepositionwithcleanbedconditionusingdifferenttreebasedalgorithms AT alitorabihaghighi sedimenttransportmodelinginnondepositionwithcleanbedconditionusingdifferenttreebasedalgorithms AT alidanandehmehr sedimenttransportmodelinginnondepositionwithcleanbedconditionusingdifferenttreebasedalgorithms
_version_	1718374387315376128

Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms.

Ejemplares similares