Forecasting Local Mean Sea Level by Generalized Behavioral Learning Method

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dc.authoridERTUGRUL, Ömer Faruk/0000-0003-0710-0867
dc.authoridTağluk, M. Emin/0000-0001-7789-6376
dc.authorwosidERTUGRUL, Ömer Faruk/F-7057-2015
dc.authorwosidTağluk, M. Emin/ABH-1005-2020
dc.contributor.authorErtugrul, Omer Faruk
dc.contributor.authorTagluk, Mehmet Emin
dc.date.accessioned2024-08-04T20:43:55Z
dc.date.available2024-08-04T20:43:55Z
dc.date.issued2017
dc.departmentİnönü Üniversitesien_US
dc.description.abstractDetermining and forecasting the local mean sea level (MSL), which is a major indicator of global warming, is an essential issue to set public policies to save our future. Owing to its importance, MSL values are measured and shared periodically by many agencies. It is not easy to model or forecast MSL because it depends on many dynamic sources such as global warming, geophysical phenomena, and circulations in the ocean and atmosphere. Several of researchers applied and recommended employing artificial neural network (ANN) in the estimation of MSL. However, ANN does not take into account the order of samples, which may consist essential information. In this study, the generalized behavioral learning method (GBLM), which is based on behavioral learning theories, was employed in order to achieve higher accuracies by using samples in the training dataset and the order of samples. To evaluate and validate GBLM, MSL of seven stations around the world was picked up. These datasets were employed to forecast the local MSL for the future. Obtained results were compared with the ones obtained by ANN that is trained by extreme learning machine and the literature. The GBLM is found to be successful in terms of the achieved high accuracies and the ability to tracking trends and fluctuations of a local MSL.en_US
dc.identifier.doi10.1007/s13369-017-2468-4
dc.identifier.endpage3298en_US
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-85024836202en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3289en_US
dc.identifier.urihttps://doi.org/10.1007/s13369-017-2468-4
dc.identifier.urihttps://hdl.handle.net/11616/97897
dc.identifier.volume42en_US
dc.identifier.wosWOS:000405808200016en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofArabian Journal For Science and Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMean sea levelen_US
dc.subjectGeneralized behavioral learning methoden_US
dc.subjectExtreme learning machineen_US
dc.subjectPSMSL databaseen_US
dc.titleForecasting Local Mean Sea Level by Generalized Behavioral Learning Methoden_US
dc.typeArticleen_US

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