Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/1445
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dc.contributor.authorTütüncü, Kemal-
dc.contributor.authorŞahman, Mehmet Akif-
dc.contributor.authorTuşat, Ekrem-
dc.date.accessioned2021-12-13T10:41:22Z-
dc.date.available2021-12-13T10:41:22Z-
dc.date.issued2021-
dc.identifier.issn1568-4946-
dc.identifier.issn1872-9681-
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2021.107444-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/1445-
dc.description.abstractModeling and optimization from natural phenomena and observations of the physical earth is an extremely important issue. In the light of the developments in computer and artificial intelligence technologies, the applications of learning-based modeling and optimization techniques in all kinds of study fields are increasing. In this research, the applicability of four different state-of-the-art metaheuristic algorithms which are Particle swarm optimization (PSO), Tree-Seed Algorithm (TSA), Artificial Bee Colony (ABC) algorithm, and Grey Wolf Optimizer (GWO), in local GNSS/leveling geoid studies have been examined. The most suitable geoid model has been tried to be obtained by using different reference points via the well-known machine learning algorithms, Artificial Neural Network (ANN) and Extreme Learning Machine (ELM), at the existing GNSS/leveling points in Burdur city of Turkey. In this study, eight different hybrid approaches are proposed by using each metaheuristic algorithm together with machine learning methods. By using these hybrid approaches, the model closest to the minimum number of reference points has been tried to be obtained. Furthermore, the performance of the hybrid approaches has been compared. According to the comparisons, the hybrid approach performed with GWO and ELM has achieved better results than other proposed hybrid approaches. As a result of the research, it has been seen that the most suitable local GNSS/Leveling geoid can be determined with a lower number of reference points in an appropriate distribution. (C) 2021 Elsevier B.V. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherELSEVIERen_US
dc.relation.ispartofAPPLIED SOFT COMPUTINGen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGnss/Leveling Geoiden_US
dc.subjectModelingen_US
dc.subjectOptimizationen_US
dc.subjectGrey Wolf Optimizeren_US
dc.subjectExtreme Learning Machineen_US
dc.subjectArtificial Bee Colonyen_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectTree-Seed Algorithmen_US
dc.subjectRadial Basis Functionsen_US
dc.subjectNeural-Networken_US
dc.subjectModelen_US
dc.subjectPsoen_US
dc.subjectClassificationen_US
dc.subjectPolynomialsen_US
dc.subjectSingleen_US
dc.titleA hybrid binary grey wolf optimizer for selection and reduction of reference points with extreme learning machine approach on local GNSS/leveling geoid determinationen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.asoc.2021.107444-
dc.identifier.scopus2-s2.0-85105331542en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Harita Mühendisliği Bölümüen_US
dc.authoridTutuncu, Kemal/0000-0002-3005-374X-
dc.authorwosidTutuncu, Kemal/A-3000-2016-
dc.identifier.volume108en_US
dc.identifier.wosWOS:000670068500002en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorscopusid16029886700-
dc.authorscopusid43361772800-
dc.authorscopusid36161595400-
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextembargo_20300101-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.08. Department of Geomatic Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
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