AN OUTLIER DETECTION METHOD IN GEODETIC NETWORKS BASED ON THE ORIGINAL OBSERVATIONS

Detalhes bibliográficos
Autor(a) principal: ERDOGAN, BAHATTIN
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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spelling 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
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