Online sequential, outlier robust, and parallel layer perceptron extreme learning machine models for sediment transport in sewer pipes

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dc.authoridGUL, ENES/0000-0001-9364-9738
dc.authoridSafari, Mir Jafar Sadegh/0000-0003-0559-5261
dc.authorwosidGUL, ENES/AAH-6191-2021
dc.authorwosidSafari, Mir Jafar Sadegh/A-4094-2019
dc.contributor.authorKouzehkalani Sales, Ali
dc.contributor.authorGul, Enes
dc.contributor.authorSafari, Mir Jafar Sadegh
dc.date.accessioned2024-08-04T20:53:21Z
dc.date.available2024-08-04T20:53:21Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractSediment transport is a noteworthy task in the design and operation of sewer pipes. Decreasing sewer pipe hydraulic capacity and transport of pollution are the main consequences of continuous sedimentation. Among different design approaches, the non-deposition with deposited bed (NDB) method can be used for the design of large sewer pipes; however, existing models are established on limited data ranges and mostly applied conventional regression methods. The current study improves the NDB sediment transport modeling by utilizing wide data ranges, and furthermore, applying robust machine learning techniques. In the present study, the conventional extreme learning machine (ELM) technique and its advanced versions, namely the online sequential-extreme learning machine (OS-ELM), outlier robust-extreme learning machine (OR-ELM), and parallel layer perceptron-extreme learning machine (PLP-ELM) are used for the modeling. In the studies conducted in the literature, sediment deposited bed thickness (t(s)) or deposited bed width (W-b) was used in the model structure as a deposited sediment variable, and therefore, different parameters in terms of t(s) and W-b can be incorporated into the model structure. However, an uncertainty arises in the selection of the appropriate parameter among W-b/Y, t(s)/Y, W-b/D, and t(s)/D (Y is flow depth and D circular pipe diameter). In order to define the most appropriate parameter to best describe the impact of deposited sediment at the channel bottom in the modeling procedure, four various scenarios using four different parameters that incorporate deposited sediment variables at their structures as W-b/Y, t(s)/Y, W/D, and t(s)/D are considered for model development. It is found that models that incorporate sediment bed thickness (t(s)) provide better results than those which use deposited bed width (W-b) in their structures. Among four different scenarios, models that utilized t(s)/D dimensionless parameter, give superior results in contrast to their alternatives. Based on the outcomes, the OR-ELM approach outperformed ELM, OS-ELM, and PLP-ELM techniques. The results obtained from applied methods are compared to their corresponding models in the literature, indicating the superiority of the OR-ELM model. It is figured out that the thickness of the deposited bed is an effective variable in modeling NDB sediment transport in sewer pipes.en_US
dc.description.sponsorshipFundamental Research Funds for the Central Universities [SWU2209237]en_US
dc.description.sponsorshipThis research is sponsored by Fundamental Research Funds for the Central Universities (SWU2209237) and Innovation Research 2035 Pilot Plan of Southwesten_US
dc.identifier.doi10.1007/s11356-022-24989-0
dc.identifier.endpage39652en_US
dc.identifier.issn0944-1344
dc.identifier.issn1614-7499
dc.identifier.issue14en_US
dc.identifier.pmid36596972en_US
dc.identifier.scopus2-s2.0-85145501399en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage39637en_US
dc.identifier.urihttps://doi.org/10.1007/s11356-022-24989-0
dc.identifier.urihttps://hdl.handle.net/11616/101096
dc.identifier.volume30en_US
dc.identifier.wosWOS:000907103500006en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofEnvironmental Science and Pollution Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeposited beden_US
dc.subjectExtreme learning machineen_US
dc.subjectOnline sequentialen_US
dc.subjectOutlier robusten_US
dc.subjectParallel layer perceptronen_US
dc.subjectSediment transporten_US
dc.titleOnline sequential, outlier robust, and parallel layer perceptron extreme learning machine models for sediment transport in sewer pipesen_US
dc.typeArticleen_US

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