Ensemble and optimized hybrid algorithms through Runge Kutta optimizer for sewer sediment transport modeling using a data pre-processing approach

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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.contributor.authorDursun, Omer Faruk
dc.contributor.authorTayfur, Gokmen
dc.date.accessioned2024-08-04T20:54:40Z
dc.date.available2024-08-04T20:54:40Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractUncontrolled sediment deposition in drainage and sewer systems raises unexpected maintenance expenditures. To this end, implementation of an accurate model relying on effective parameters involved is a reliable benchmark. In this study, three machine learning techniques, namely extreme learning machine (ELM), multilayer perceptron neural network (MLPNN), and M5P model tree (M5PMT); and three optimization approaches of Runge Kutta (RUN), genetic algorithm (GA), and particle swarm optimization (PSO) are applied for modeling. The optimization and ensemble hybridization approaches are applied in the modeling procedure. For the case of hybrid optimized models, the ELM and MLPNN models are hybridized with RUN, GA, and PSO algorithms to develop six hybrid models of ELM-RUN, ELM-GA, ELMPSO, MLPNN-RUN, MLPNN-GA, and MLPNN-PSO. Ensemble hybrid models are developed through coupling the ELM and MLPNN models with the M5PMT algorithm. The data pre-processing approach is applied to find the best randomness characteristic of the utilized data. Results illustrate that the RUNbased hybrid models outperform the GA- and PSO-based counterparts. Although the MLPNN-RUN and MLPNN-M5PMT hybrid models generate better results than their alternatives, MLPNN-M5PMT slightly outperforms MLPNN-RUN model with a coefficient of determination of 0.84 and a root mean square error of 0.88. The current study shows the superiority of the ensemble-based approach to the optimization techniques. Further investigation is needed by considering alternative optimization techniques to enhance sediment transport modeling. (c) 2023 International Research and Training Centre on Erosion and Sedimentation/the World Association for Sedimentation and Erosion Research. Published by Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.ijsrc.2023.07.003
dc.identifier.endpage858en_US
dc.identifier.issn1001-6279
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85170540978en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage847en_US
dc.identifier.urihttps://doi.org/10.1016/j.ijsrc.2023.07.003
dc.identifier.urihttps://hdl.handle.net/11616/101567
dc.identifier.volume38en_US
dc.identifier.wosWOS:001101270000001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIrtcesen_US
dc.relation.ispartofInternational Journal of Sediment Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEnsemble learningen_US
dc.subjectHybrid modelen_US
dc.subjectMachine learningen_US
dc.subjectOpen channelsen_US
dc.subjectSediment transporten_US
dc.subjectSewer pipesen_US
dc.titleEnsemble and optimized hybrid algorithms through Runge Kutta optimizer for sewer sediment transport modeling using a data pre-processing approachen_US
dc.typeArticleen_US

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