Forecasting local mean sea level by generalized behavioral learning method

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dc.authoridErtuğrul, Ömer Faruken_US
dc.authorid0000-0001-7789-6376en_US
dc.contributor.authorErtuğrul, Ömer Faruk
dc.contributor.authorTağluk, Mehmet Emin
dc.date.accessioned2019-07-04T13:11:02Z
dc.date.available2019-07-04T13:11:02Z
dc.date.issued2017-03-13en_US
dc.departmentBatman Üniversitesi Mühendislik - Mimarlık Fakültesi Elektrik-Elektronik Mühendisliği Bölümüen_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.citationErtuğrul, Ö F., Tağluk, M. E. (2017). Forecasting local mean sea level by generalized behavioral learning method. Arabian Journal for Science and Engineering, 42(8), pp. 3289-3298. https://doi.org/10.1007/s13369-017-2468-4en_US
dc.identifier.endpage3298en_US
dc.identifier.issn1319-8025
dc.identifier.issn2191-4281
dc.identifier.issue8en_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/20.500.12402/2186
dc.identifier.volume42en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.relation.isversionof10.1007/s13369-017-2468-4en_US
dc.relation.journalArabian Journal for Science and Engineeringen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectExtreme Learning Machineen_US
dc.subjectGeneralized Behavioral Learning Methoden_US
dc.subjectMean Sea Levelen_US
dc.subjectPSMSL Databaseen_US
dc.titleForecasting local mean sea level by generalized behavioral learning methoden_US
dc.typeArticleen_US

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