An approach to estimate occupational accidents using least-squares support vector machines

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dc.contributor.authorCeylan, Huseyin
dc.contributor.authorParlakyildiz, Sakir
dc.date.accessioned2020-06-25T18:22:53Z
dc.date.available2020-06-25T18:22:53Z
dc.date.issued2017
dc.departmentKırıkkale Üniversitesi
dc.descriptionparlakyildiz, sakir/0000-0003-0885-023X
dc.description.abstractLeast-squares support vector machines represent an emerging technique that has been adopted to estimate accidents. In this study, occupational accident estimation models were developed using the least-squares support vector machine method for the Republic of Turkey. In addition, linear regression analysis, nonlinear regression analysis, and artificial neural network models were considered. During the development phase of the models, statistical data from 1970 to 2012 were used to consider parameters such as insured workers, workplaces, occupational accidents, deaths, and permanent incapacities. Using these models, the numbers of accidents, deaths, and permanent incapacities resulting from occupational accidents were estimated for three different scenarios in the Republic of Turkey through the end of 2025. The performances of the developed models were evaluated considering the mean absolute percent errors and the mean absolute errors. In addition, we compared the least-squares support vector machine, linear regression analysis, nonlinear regression analysis, and artificial neural network methods in terms of their estimation performances. Our simulation results demonstrate that the proposed least-squares support vector machine model outperforms other techniques in terms of accuracy and has a rapid convergence capability when estimating occupational accidents.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.endpage91en_US
dc.identifier.issn2307-4108
dc.identifier.issn2307-4116
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85036502570
dc.identifier.scopusqualityQ2
dc.identifier.startpage83en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12587/6941
dc.identifier.volume44en_US
dc.identifier.wosWOS:000412120400010
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAcademic Publication Councilen_US
dc.relation.ispartofKuwait Journal Of Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAccident prediction modelsen_US
dc.subjectartificial neural networksen_US
dc.subjectestimation erroren_US
dc.subjectleast squares support vector machineen_US
dc.subjectoccupational accidenten_US
dc.titleAn approach to estimate occupational accidents using least-squares support vector machinesen_US
dc.typeArticle

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