Robust optimization of SVM hyper-parameters for spillway type selection

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dc.authoridALPASLAN, Nuh/0000-0002-6828-755X
dc.authoridGUL, ENES/0000-0001-9364-9738;
dc.authorwosidALPASLAN, Nuh/AAA-4227-2022
dc.authorwosidGUL, ENES/AAH-6191-2021
dc.authorwosidEmiroglu, Muhammet/V-9055-2018
dc.contributor.authorGul, Enes
dc.contributor.authorAlpaslan, Nuh
dc.contributor.authorEmiroglu, M. Emin
dc.date.accessioned2024-08-04T20:49:19Z
dc.date.available2024-08-04T20:49:19Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractSpillways, which play a vital role in dams, can be built in various types. Although several studies have been conducted on hydraulic calculations of spillways, studies on type selection that require heuristics knowledge were limited. The tuning of the hyperparameters in machine learning algorithms is still an open problem. In this paper, a parallel global optimization algorithm is proposed optimizing the hyper-parameters of a Support Vector Machine (SVM) classification model for providing accurate spillway type selection (STS). The random forest method is used to obtain the relative importance of input variables. Besides, a novel spillway dataset was introduced and a novel STS software tool has been developed based on different machine learning algorithms. Several experiments are carried out to demonstrate the effectiveness of the proposed tool and the reliability of data. The hyper-parameters optimized SVM was achieved the best results with 93.81% classification accuracy. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University.en_US
dc.identifier.doi10.1016/j.asej.2020.10.022
dc.identifier.endpage2423en_US
dc.identifier.issn2090-4479
dc.identifier.issn2090-4495
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85101590290en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2413en_US
dc.identifier.urihttps://doi.org/10.1016/j.asej.2020.10.022
dc.identifier.urihttps://hdl.handle.net/11616/99788
dc.identifier.volume12en_US
dc.identifier.wosWOS:000700578200002en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofAin Shams Engineering Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEnergy dissipationen_US
dc.subjectDam typeen_US
dc.subjectHyper-parameter optimizationen_US
dc.subjectSupport vector machineen_US
dc.subjectHydraulic structureen_US
dc.titleRobust optimization of SVM hyper-parameters for spillway type selectionen_US
dc.typeArticleen_US

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