Robust low-rank learning multi-output regression for incipient sediment motion in sewer pipes

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buir.contributor.authorRahimzadeh Arashloo, Shervin
buir.contributor.orcidRahimzadeh Arashloo, Shervin|0000-0003-0189-4774
dc.citation.epage870en_US
dc.citation.issueNumber6
dc.citation.spage859
dc.citation.volumeNumber38
dc.contributor.authorSafari, M. J. S.
dc.contributor.authorRahimzadeh Arashloo, Shervin
dc.date.accessioned2024-03-13T08:01:37Z
dc.date.available2024-03-13T08:01:37Z
dc.date.issued2023-08-30
dc.departmentDepartment of Computer Engineering
dc.description.abstractThe existing incipient sediment motion models typically apply conventional regression methods considering either velocity or shear stress. In the current study, incipient sediment motion is analyzed through a simultaneous and joint analysis of velocity and shear stress using the robust low-rank learning (RLRL) multi-output regression technique. Moreover, the experimental data compiled from five different channels are utilized to develop a generic incipient sediment motion model valid for a channel of any cross-sectional shape. The efficiency of the developed method is examined and compared against the available conventional regression models. The experimental results indicate that the RLRL model yields better results than its counterparts. In particular, while cross-section specific models fail to provide accurate estimates for shear stress or velocity for other cross sections, the proposed model provides satisfactory results for all channel shapes. The better performance of the recommended approach can be attributed to the joint modeling of the shear stress and the velocity which is realized by capturing the correlation between these parameters in terms of a low rank output mixing matrix which enhances the prediction performance of the approach.
dc.description.provenanceMade available in DSpace on 2024-03-13T08:01:37Z (GMT). No. of bitstreams: 1 Robust_low-rank_learning_multi-output_regression_for_incipient_sediment_motion_in_sewer_pipes.pdf: 3192633 bytes, checksum: 580be864a68586e1edc7c718d9bf790f (MD5) Previous issue date: 2023-08-30en
dc.identifier.doi10.1016/j.ijsrc.2023.08.004
dc.identifier.eissn2589-7284
dc.identifier.issn1001-6279
dc.identifier.urihttps://hdl.handle.net/11693/114663
dc.language.isoen
dc.publisherElsevier BV
dc.relation.isversionofhttps://doi.org/10.1016/j.ijsrc.2023.08.004
dc.rightsCC BY 4.0 DEED (Attribution 4.0 International)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.source.titleInternational Journal of Sediment Research
dc.subjectLow-rank learning
dc.subjectMulti-output regression
dc.subjectSediment transport
dc.subjectSewer flow
dc.subjectShear stress approach
dc.subjectVelocity approach
dc.titleRobust low-rank learning multi-output regression for incipient sediment motion in sewer pipes
dc.typeArticle

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