AN OUTLIER DETECTION METHOD IN GEODETIC NETWORKS BASED ON THE ORIGINAL OBSERVATIONS
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Boletim de Ciências Geodésicas |
Texto Completo: | https://revistas.ufpr.br/bcg/article/view/37848 |
Resumo: | The observations in geodetic networks are measured repetitively and in the networkadjustment step, the mean values of these original observations are used. The meanoperator is a kind of Least Square Estimation (LSE). LSE provides optimal resultswhen random errors are normally distributed. If one of the original repetitiveobservations has outlier, the magnitude of this outlier will decrease because themean value of these original observations is used in the network adjustment andoutlier detection. In this case, the reliability of the outlier detection methodsdecreases, too. Since the original repetitive observations are independent, they canbe used in the adjustment model instead of the estimating mean value of them. Inthis study, to show the effects of the estimating mean value of the original repetitiveobservations, a leveling network that contains both outward run and backward runobservations were simulated. Tests for outlier, Huber and Danish methods wereapplied to two different cases. First, the mean values of the original observations(outward run and return run) were used; and then all original observations wereconsidered in the outlier detection. The reliabilities of the methods were measuredby Mean Succes Rate. According to the obtained results, the second case has morereliable results than first case. |
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Boletim de Ciências Geodésicas |
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AN OUTLIER DETECTION METHOD IN GEODETIC NETWORKS BASED ON THE ORIGINAL OBSERVATIONSOutlier DetectionOriginal ObservationsTests for OutlierRobust MethodReliabilityThe observations in geodetic networks are measured repetitively and in the networkadjustment step, the mean values of these original observations are used. The meanoperator is a kind of Least Square Estimation (LSE). LSE provides optimal resultswhen random errors are normally distributed. If one of the original repetitiveobservations has outlier, the magnitude of this outlier will decrease because themean value of these original observations is used in the network adjustment andoutlier detection. In this case, the reliability of the outlier detection methodsdecreases, too. Since the original repetitive observations are independent, they canbe used in the adjustment model instead of the estimating mean value of them. Inthis study, to show the effects of the estimating mean value of the original repetitiveobservations, a leveling network that contains both outward run and backward runobservations were simulated. Tests for outlier, Huber and Danish methods wereapplied to two different cases. First, the mean values of the original observations(outward run and return run) were used; and then all original observations wereconsidered in the outlier detection. The reliabilities of the methods were measuredby Mean Succes Rate. According to the obtained results, the second case has morereliable results than first case.Boletim de Ciências GeodésicasBulletin of Geodetic Sciences2014-09-19info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufpr.br/bcg/article/view/3784810.5380/bcg.v20i3.37848Boletim de Ciências Geodésicas; v. 20 n. 3 (2014)Bulletin of Geodetic Sciences; Vol. 20 No. 3 (2014)1982-21701413-485310.5380/bcg.v20i3reponame:Boletim de Ciências Geodésicasinstname:Universidade Federal do Paraná (UFPR)instacron:UFPRporhttps://revistas.ufpr.br/bcg/article/view/37848/23147ERDOGAN, BAHATTINinfo:eu-repo/semantics/openAccess2014-09-30T03:00:00Zoai:ojs.pkp.sfu.ca:article/37848Revistahttps://revistas.ufpr.br/bcgPUBhttps://revistas.ufpr.br/bcg/oaiqdalmolin@ufpr.br|| danielsantos@ufpr.br||qdalmolin@ufpr.br|| danielsantos@ufpr.br|| bcg_editor@ufpr.br1982-21701413-4853opendoar:2014-09-30T03:00Boletim de Ciências Geodésicas - Universidade Federal do Paraná (UFPR)false |
dc.title.none.fl_str_mv |
AN OUTLIER DETECTION METHOD IN GEODETIC NETWORKS BASED ON THE ORIGINAL OBSERVATIONS |
title |
AN OUTLIER DETECTION METHOD IN GEODETIC NETWORKS BASED ON THE ORIGINAL OBSERVATIONS |
spellingShingle |
AN OUTLIER DETECTION METHOD IN GEODETIC NETWORKS BASED ON THE ORIGINAL OBSERVATIONS ERDOGAN, BAHATTIN Outlier Detection Original Observations Tests for Outlier Robust Method Reliability |
title_short |
AN OUTLIER DETECTION METHOD IN GEODETIC NETWORKS BASED ON THE ORIGINAL OBSERVATIONS |
title_full |
AN OUTLIER DETECTION METHOD IN GEODETIC NETWORKS BASED ON THE ORIGINAL OBSERVATIONS |
title_fullStr |
AN OUTLIER DETECTION METHOD IN GEODETIC NETWORKS BASED ON THE ORIGINAL OBSERVATIONS |
title_full_unstemmed |
AN OUTLIER DETECTION METHOD IN GEODETIC NETWORKS BASED ON THE ORIGINAL OBSERVATIONS |
title_sort |
AN OUTLIER DETECTION METHOD IN GEODETIC NETWORKS BASED ON THE ORIGINAL OBSERVATIONS |
author |
ERDOGAN, BAHATTIN |
author_facet |
ERDOGAN, BAHATTIN |
author_role |
author |
dc.contributor.author.fl_str_mv |
ERDOGAN, BAHATTIN |
dc.subject.por.fl_str_mv |
Outlier Detection Original Observations Tests for Outlier Robust Method Reliability |
topic |
Outlier Detection Original Observations Tests for Outlier Robust Method Reliability |
description |
The observations in geodetic networks are measured repetitively and in the networkadjustment step, the mean values of these original observations are used. The meanoperator is a kind of Least Square Estimation (LSE). LSE provides optimal resultswhen random errors are normally distributed. If one of the original repetitiveobservations has outlier, the magnitude of this outlier will decrease because themean value of these original observations is used in the network adjustment andoutlier detection. In this case, the reliability of the outlier detection methodsdecreases, too. Since the original repetitive observations are independent, they canbe used in the adjustment model instead of the estimating mean value of them. Inthis study, to show the effects of the estimating mean value of the original repetitiveobservations, a leveling network that contains both outward run and backward runobservations were simulated. Tests for outlier, Huber and Danish methods wereapplied to two different cases. First, the mean values of the original observations(outward run and return run) were used; and then all original observations wereconsidered in the outlier detection. The reliabilities of the methods were measuredby Mean Succes Rate. According to the obtained results, the second case has morereliable results than first case. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-09-19 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://revistas.ufpr.br/bcg/article/view/37848 10.5380/bcg.v20i3.37848 |
url |
https://revistas.ufpr.br/bcg/article/view/37848 |
identifier_str_mv |
10.5380/bcg.v20i3.37848 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://revistas.ufpr.br/bcg/article/view/37848/23147 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Boletim de Ciências Geodésicas Bulletin of Geodetic Sciences |
publisher.none.fl_str_mv |
Boletim de Ciências Geodésicas Bulletin of Geodetic Sciences |
dc.source.none.fl_str_mv |
Boletim de Ciências Geodésicas; v. 20 n. 3 (2014) Bulletin of Geodetic Sciences; Vol. 20 No. 3 (2014) 1982-2170 1413-4853 10.5380/bcg.v20i3 reponame:Boletim de Ciências Geodésicas instname:Universidade Federal do Paraná (UFPR) instacron:UFPR |
instname_str |
Universidade Federal do Paraná (UFPR) |
instacron_str |
UFPR |
institution |
UFPR |
reponame_str |
Boletim de Ciências Geodésicas |
collection |
Boletim de Ciências Geodésicas |
repository.name.fl_str_mv |
Boletim de Ciências Geodésicas - Universidade Federal do Paraná (UFPR) |
repository.mail.fl_str_mv |
qdalmolin@ufpr.br|| danielsantos@ufpr.br||qdalmolin@ufpr.br|| danielsantos@ufpr.br|| bcg_editor@ufpr.br |
_version_ |
1821142532588830720 |