Regional mapping of the Geoid Using GNSS (GPS) measurements and an artiticial neural network
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/267608 |
Resumo: | The determination of the orthometric height from geometric leveling has practical difficulties that, despite a number of scientific and technological advances, passed a century without substantial modifications or advances. Currently, the Global Navigation Satellite System (GNSS) has been used with reasonable success for orthometric height determination. With a sufficient number of benchmarks with known horizontal and vertical coordinates, it is often possible to adjust using the least squares method mathematical expressions that allow interpolation of geoid heights. The objective of this study is to present an alternative method to interpolate geoid heights based on the technique of Artificial Neural Networks (ANNs). The study area is the Brazilian state of São Paulo, and for training the ANN the authors have used geoid height information from the EGM08 gravity model with a grid spacing of 10 minutes of arc. The efficiency of the model was tested at 157 points with known geoid heights distributed across the study area. The results were also compared with the Brazilian Geoid Model (MAPGEO2004). Based on those 157 benchmarks it was possible to verify that the model generated by ANNs provided a mean absolute error of 0.24 m in obtaining a geoid height value. Statistical tests have shown that there was no difference between the means from known geoid heights and geoid heights provided by the neural model for a significance level of 5%. It was also found that ANNs provided an improvement of 2.7 times in geoid height estimates when compared with the MAPGEO2004 geoid model. |
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Veronez, Maurício RobertoSouza, Sergio Florencio deMatsuoka, Marcelo TomioReinhardt, Alessandro OttSilva, Reginaldo Macedônio da2023-11-25T03:26:03Z20112072-4292http://hdl.handle.net/10183/267608000815902The determination of the orthometric height from geometric leveling has practical difficulties that, despite a number of scientific and technological advances, passed a century without substantial modifications or advances. Currently, the Global Navigation Satellite System (GNSS) has been used with reasonable success for orthometric height determination. With a sufficient number of benchmarks with known horizontal and vertical coordinates, it is often possible to adjust using the least squares method mathematical expressions that allow interpolation of geoid heights. The objective of this study is to present an alternative method to interpolate geoid heights based on the technique of Artificial Neural Networks (ANNs). The study area is the Brazilian state of São Paulo, and for training the ANN the authors have used geoid height information from the EGM08 gravity model with a grid spacing of 10 minutes of arc. The efficiency of the model was tested at 157 points with known geoid heights distributed across the study area. The results were also compared with the Brazilian Geoid Model (MAPGEO2004). Based on those 157 benchmarks it was possible to verify that the model generated by ANNs provided a mean absolute error of 0.24 m in obtaining a geoid height value. Statistical tests have shown that there was no difference between the means from known geoid heights and geoid heights provided by the neural model for a significance level of 5%. It was also found that ANNs provided an improvement of 2.7 times in geoid height estimates when compared with the MAPGEO2004 geoid model.application/pdfengRemote sensing. Basel : MDPI AG, 2011. Vol. 3, n. 4 (Apr. 2011), p. 668-683Sensoriamento remotoRedes neurais artificiaisGeoid heightEarth gravitational model 2008Artificial neural networksRegional mapping of the Geoid Using GNSS (GPS) measurements and an artiticial neural networkEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT000815902.pdf.txt000815902.pdf.txtExtracted Texttext/plain38287http://www.lume.ufrgs.br/bitstream/10183/267608/2/000815902.pdf.txta19b861656cb9984dc00f03ff8ed139bMD52ORIGINAL000815902.pdfTexto completo (inglês)application/pdf1996474http://www.lume.ufrgs.br/bitstream/10183/267608/1/000815902.pdf605d1137998e03df5baee126cd52f20bMD5110183/2676082023-12-06 04:24:31.405637oai:www.lume.ufrgs.br:10183/267608Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-12-06T06:24:31Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Regional mapping of the Geoid Using GNSS (GPS) measurements and an artiticial neural network |
title |
Regional mapping of the Geoid Using GNSS (GPS) measurements and an artiticial neural network |
spellingShingle |
Regional mapping of the Geoid Using GNSS (GPS) measurements and an artiticial neural network Veronez, Maurício Roberto Sensoriamento remoto Redes neurais artificiais Geoid height Earth gravitational model 2008 Artificial neural networks |
title_short |
Regional mapping of the Geoid Using GNSS (GPS) measurements and an artiticial neural network |
title_full |
Regional mapping of the Geoid Using GNSS (GPS) measurements and an artiticial neural network |
title_fullStr |
Regional mapping of the Geoid Using GNSS (GPS) measurements and an artiticial neural network |
title_full_unstemmed |
Regional mapping of the Geoid Using GNSS (GPS) measurements and an artiticial neural network |
title_sort |
Regional mapping of the Geoid Using GNSS (GPS) measurements and an artiticial neural network |
author |
Veronez, Maurício Roberto |
author_facet |
Veronez, Maurício Roberto Souza, Sergio Florencio de Matsuoka, Marcelo Tomio Reinhardt, Alessandro Ott Silva, Reginaldo Macedônio da |
author_role |
author |
author2 |
Souza, Sergio Florencio de Matsuoka, Marcelo Tomio Reinhardt, Alessandro Ott Silva, Reginaldo Macedônio da |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Veronez, Maurício Roberto Souza, Sergio Florencio de Matsuoka, Marcelo Tomio Reinhardt, Alessandro Ott Silva, Reginaldo Macedônio da |
dc.subject.por.fl_str_mv |
Sensoriamento remoto Redes neurais artificiais |
topic |
Sensoriamento remoto Redes neurais artificiais Geoid height Earth gravitational model 2008 Artificial neural networks |
dc.subject.eng.fl_str_mv |
Geoid height Earth gravitational model 2008 Artificial neural networks |
description |
The determination of the orthometric height from geometric leveling has practical difficulties that, despite a number of scientific and technological advances, passed a century without substantial modifications or advances. Currently, the Global Navigation Satellite System (GNSS) has been used with reasonable success for orthometric height determination. With a sufficient number of benchmarks with known horizontal and vertical coordinates, it is often possible to adjust using the least squares method mathematical expressions that allow interpolation of geoid heights. The objective of this study is to present an alternative method to interpolate geoid heights based on the technique of Artificial Neural Networks (ANNs). The study area is the Brazilian state of São Paulo, and for training the ANN the authors have used geoid height information from the EGM08 gravity model with a grid spacing of 10 minutes of arc. The efficiency of the model was tested at 157 points with known geoid heights distributed across the study area. The results were also compared with the Brazilian Geoid Model (MAPGEO2004). Based on those 157 benchmarks it was possible to verify that the model generated by ANNs provided a mean absolute error of 0.24 m in obtaining a geoid height value. Statistical tests have shown that there was no difference between the means from known geoid heights and geoid heights provided by the neural model for a significance level of 5%. It was also found that ANNs provided an improvement of 2.7 times in geoid height estimates when compared with the MAPGEO2004 geoid model. |
publishDate |
2011 |
dc.date.issued.fl_str_mv |
2011 |
dc.date.accessioned.fl_str_mv |
2023-11-25T03:26:03Z |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
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info:eu-repo/semantics/publishedVersion |
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article |
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http://hdl.handle.net/10183/267608 |
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2072-4292 |
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000815902 |
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http://hdl.handle.net/10183/267608 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Remote sensing. Basel : MDPI AG, 2011. Vol. 3, n. 4 (Apr. 2011), p. 668-683 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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