Univariate Approach for Detecting Outliers in Geodetic Networks

Hekimoğlu, Şerif; Erdoğan, Bahattin; Soycan, Metin; Durdağ, Utkan Mustafa

doi:10.1061/(asce)su.1943-5428.0000123

Cited by 29 publications

(24 citation statements)

References 18 publications

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“…In the first step, the monitoring network is adjusted as a free network for each epoch. The corresponding GaussÀMarkov model for geodetic networks is given as follows (Caspary 1988;Hekimoglu et al 2014):…”

Section: Processing Of Geodetic Observations and Deformation Analysismentioning

confidence: 99%

Displacement response of a concrete arch dam to seasonal temperature fluctuations and reservoir level rise during the first filling period: evidence from geodetic data

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Ceylan

2015

Geomatics, Natural Hazards and Risk

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The present study evaluates the dynamic behaviour of the Ermenek Dam, the second highest dam in Turkey, based on conventional geodetic measurements and Finite Element Model (FEM) analyses during its first filling period. In total, eight periods of measured deformation are considered from the end of construction until the reservoir reached its full capacity. The displacement response of the dam to the reservoir level and to seasonal temperature variations is examined in detail. Time series of apparent total displacements at the middle of the crest of the dam exhibits periodicity and linear trends. Correlation analysis revealed that periodic and linear displacement responses of the dam are related to variations of seasonal temperature and linearly increased reservoir level, respectively, indicating a relation between temperature, water load and dam deformation. It is also concluded that measured deformations based on geodetic data show good agreement with the predicted deformation obtained by the FEM analysis.

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Section: Processing Of Geodetic Observations and Deformation Analysismentioning

confidence: 99%

Displacement response of a concrete arch dam to seasonal temperature fluctuations and reservoir level rise during the first filling period: evidence from geodetic data

Yiğit

Alçay

Ceylan

2015

Geomatics, Natural Hazards and Risk

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“…When a weight matrix is chosen as the inverse of observables’ covariance matrix, the weighted least-squares (WLS) estimation is the best linear unbiased estimator (BLUE), assuming that no outlier exists. However, outliers can inevitably occur in practice and cause the optimal feature of such estimation loss [1,2,3]. Therefore, outliers must be detected and then eliminated as soon as possible.…”

Section: Introductionmentioning

confidence: 99%

Specific Direction-Based Outlier Detection Approach for GNSS Vector Networks

Shen

Yang

Shen

2019

Sensors

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In this paper we propose an outlier detection approach for GNSS vector networks based on the specific direction (i.e., SD approach), along which the test statistic constructed reaches the maximum. We derive the unit vector of this specific direction in detail, and prove that the unit vector is the same as that determined by the outlier estimates in three-dimensional (3D) approach, while the distribution of the maximum test statistic in this direction is the square root of Chi-squared distribution. Therefore, eliminating an outlier along this specific direction can get the same result as that of eliminating all three components of outlier vector in 3D approach. The mathematical equivalence of SD approach and 3D approach is further demonstrated by a real GNSS network. Moreover, preliminary application of the SD approach to detect the abnormal antenna height measurement is carried out in terms of numerical simulations of multiple baseline solutions, and it shows that the SD approach can effectively detect baselines that are directly infected by corresponding receiver antenna height errors.

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“…When an outlier exists in all baseline components, the second approach is a more efficient solution while the first approach is more efficient in cases when outlier exists in only one baseline component (Saka, 2016). It is asserted and proven in practice, that in case of only one outlier, Conventional Tests are very efficient (Xu, 2005;Hekimoglu et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…The principle of the robust-M-estimation application is based on the iterative Least squares (LS) adjustment respecting the condition about gradual changes of the weights of individual observations (Třasák and Štroner, 2014). The LS is very sensitive to deviations from the model assumptions (Hampel et al, 1986) and it extends the effects of outliers to residuals of all observations (Hekimoglu et al, 2011(Hekimoglu et al, , 2014Hekimoglu and Erdogan, 2012;Erdogan, 2014). LS play an essential role in all methods of detecting outliers mentioned above.…”

Section: Introductionmentioning

confidence: 99%