A Back-Propagation Artificial Neural Network Approach for Three-Dimensional Coordinate Transformation

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dc.contributor.authorTurgut, Bayram
dc.date.accessioned2020-03-26T17:46:36Z
dc.date.available2020-03-26T17:46:36Z
dc.date.issued2010
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe European Datum 1950 (ED50) of the Turkish national geodetic network (TNGN) and the World Geodetic System 1984 (WGS84) of the Turkish national fundamental GPS network (TNFGN) are in use as geodetic reference frames in Turkey. According to the use of two reference systems, it is necessary to transform the three-dimensional (3D) coordinate data from ED50 to WGS84 or vice versa. The seven-parameter similarity transformation method is frequently used for 3D coordinate transformation in geodesy. In this study, a back propagation artificial neural network (BPANN) that has been more widely applied in engineering among all other neural network models is evaluated as an alternative 3D coordinate transformation method. BPANN is compared with a popular seven-parameter similarity transformation (Molodensky-Badekas) method over a test area, in terms of root mean square error (RMSE). The results indicated that the employment of BPANN transformed 3D coordinates (X, Y, Z) more accurate than Molodensky-Badekas method and can be useful for 3D coordinate transformation between ED50 and WGS84.en_US
dc.identifier.citationTurgut, B., (2010). A Back-Propagation Artificial Neural Network Approach for Three-Dimensional Coordinate Transformation. Scientific Research and Essays, 5(21), 3330-3335.
dc.identifier.endpage3335en_US
dc.identifier.issn1992-2248en_US
dc.identifier.issue21en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage3330en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/24495
dc.identifier.volume5en_US
dc.identifier.wosWOS:000284553700016en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorTurgut, Bayram
dc.language.isoenen_US
dc.publisherAcademic Journalsen_US
dc.relation.ispartofScientific Research and Essaysen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subject3d coordinate transformationen_US
dc.subjectBack propagation artificial neural networken_US
dc.subjectSeven-parameter similarity transformationen_US
dc.subjectBpannen_US
dc.subjectMolodensky-badekasen_US
dc.titleA Back-Propagation Artificial Neural Network Approach for Three-Dimensional Coordinate Transformationen_US
dc.typeArticleen_US

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