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

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dc.authoridDANANDEH MEHR, ALI/0000-0003-2769-106X
dc.authoridSafari, Mir Jafar Sadegh/0000-0003-0559-5261
dc.authoridGUL, ENES/0000-0001-9364-9738
dc.authorwosidDANANDEH MEHR, ALI/S-9321-2017
dc.authorwosidSafari, Mir Jafar Sadegh/A-4094-2019
dc.authorwosidGUL, ENES/AAH-6191-2021
dc.contributor.authorGul, Enes
dc.contributor.authorSafari, Mir Jafar Sadegh
dc.contributor.authorHaghighi, Ali Torabi
dc.contributor.authorMehr, Ali Danandeh
dc.date.accessioned2024-08-04T20:50:45Z
dc.date.available2024-08-04T20:50:45Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractTo 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 = 1.1 84 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.en_US
dc.identifier.doi10.1371/journal.pone.0258125
dc.identifier.issn1932-6203
dc.identifier.issue10en_US
dc.identifier.pmid34624034en_US
dc.identifier.scopus2-s2.0-85116911193en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1371/journal.pone.0258125
dc.identifier.urihttps://hdl.handle.net/11616/100261
dc.identifier.volume16en_US
dc.identifier.wosWOS:000755691200019en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPublic Library Scienceen_US
dc.relation.ispartofPlos Oneen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDesign Criteriaen_US
dc.subjectSewer Designen_US
dc.subjectPredictionen_US
dc.subjectLimiten_US
dc.subjectChannelsen_US
dc.titleSediment transport modeling in non-deposition with clean bed condition using different tree-based algorithmsen_US
dc.typeArticleen_US

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