University of Oulu

Gul E, Safari MJS, Torabi Haghighi A, Danandeh Mehr A (2021) Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms. PLoS ONE 16(10): e0258125. https://doi.org/10.1371/journal.pone.0258125

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

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Author: Gul, Enes1; Safari, Mir Jafar Sadegh2; Torabi Haghighi, Ali3;
Organizations: 1Department of Civil Engineering, Inonu University, Malatya, Turkey
2Department of Civil Engineering, Yaşar University, Izmir, Turkey
3Water, Energy and Environmental Engineering Research Unit, University of Oulu, Oulu, Finland
4Department of Civil Engineering, Antalya Bilim University, Antalya, Turkey
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.3 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021101951625
Language: English
Published: Public Library of Science, 2021
Publish Date: 2021-10-19
Description:

Abstract

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.

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Series: PLoS one
ISSN: 1932-6203
ISSN-E: 1932-6203
ISSN-L: 1932-6203
Volume: 16
Issue: 10
Article number: e0258125
DOI: 10.1371/journal.pone.0258125
OADOI: https://oadoi.org/10.1371/journal.pone.0258125
Type of Publication: A1 Journal article – refereed
Field of Science: 1171 Geosciences
Subjects:
Copyright information: © 2021 Gul et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
  https://creativecommons.org/licenses/by/4.0/