Hybrid Generalized Regularized Extreme Learning Machine Through Gradient-Based Optimizer Model for Self-Cleansing Nondeposition with Clean Bed Mode of Sediment Transport

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dc.authoridSafari, Mir Jafar Sadegh/0000-0003-0559-5261
dc.authoridGUL, ENES/0000-0001-9364-9738
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.date.accessioned2024-08-04T20:58:58Z
dc.date.available2024-08-04T20:58:58Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractSediment transport modeling is an important problem to minimize sedimentation in open channels that could lead to unexpected operation expenses. From an engineering perspective, the development of accurate models based on effective variables involved for flow velocity computation could provide a reliable solution in channel design. Furthermore, validity of sediment transport models is linked to the range of data used for the model development. Existing design models were established on the limited data ranges. Thus, the present study aimed to utilize all experimental data available in the literature, including recently published datasets that covered an extensive range of hydraulic properties. Extreme learning machine (ELM) algorithm and generalized regularized extreme learning machine (GRELM) were implemented for the modeling, and then, particle swarm optimization (PSO) and gradient-based optimizer (GBO) were utilized for the hybridization of ELM and GRELM. GRELM-PSO and GRELM-GBO findings were compared to the standalone ELM, GRELM, and existing regression models to determine their accurate computations. The analysis of the models demonstrated the robustness of the models that incorporate channel parameter. The poor results of some existing regression models seem to be linked to the disregarding of the channel parameter. Statistical analysis of the model outcomes illustrated the outperformance of GRELM-GBO in contrast to the ELM, GRELM, GRELM-PSO, and regression models, although GRELM-GBO performed slightly better when compared to the GRELM-PSO counterpart. It was found that the mean accuracy of GRELM-GBO was 18.5% better when compared to the best regression model. The promising findings of the current study not only may encourage the use of recommended algorithms for channel design in practice but also may further the application of novel ELM-based methods in alternative environmental problems.en_US
dc.identifier.doi10.1089/big.2022.0120
dc.identifier.issn2167-6461
dc.identifier.issn2167-647X
dc.identifier.pmid36881757en_US
dc.identifier.urihttps://doi.org/10.1089/big.2022.0120
dc.identifier.urihttps://hdl.handle.net/11616/103327
dc.identifier.wosWOS:000945217800001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMary Ann Liebert, Incen_US
dc.relation.ispartofBig Dataen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectextreme learning machineen_US
dc.subjectgeneralized regularized extreme learning machineen_US
dc.subjectgradient-based optimizeren_US
dc.subjectparticle swarm optimizationen_US
dc.subjectsediment transporten_US
dc.subjectself-cleansingen_US
dc.titleHybrid Generalized Regularized Extreme Learning Machine Through Gradient-Based Optimizer Model for Self-Cleansing Nondeposition with Clean Bed Mode of Sediment Transporten_US
dc.typeArticleen_US

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