Submitted as: model description paper
05 Dec 2023
Submitted as: model description paper |  | 05 Dec 2023
Status: a revised version of this preprint is currently under review for the journal GMD.

Minimal variance-based outlier detection method using forward search model error in a leveling network

Utkan Mustafa Durdağ

Abstract. Conventional and robust methods are based on the additive bias model, which may cause type-I and type-II errors. However, outliers can be regarded as additional unknown parameters in the Gauss-Markov Model. It is based on modeling the outliers as unknown parameters, considering as many combinations as possible outliers selected from the observation set. In addition, this method is expected to be more effective than conventional methods as it is based on the principle of minimal variance and removes dependency in iterations. The primary purpose of this study is to seek the novel outlier detection approach efficiency in the geodetic networks. The efficiency of the proposed model was measured and compared with the robust and conventional methods by the Mean Success Rate (MSR) indicator for different types and magnitudes of outliers. Thereby, this approach enhances the MSR by almost 40–45 % compared to the Baarda and Danish (with the variance unknown case) method for multiple outliers (i.e., 1<m<4). Besides, the Forward Search of Model Error (FSME) is 20–30 % more successful than the others in the low controllability observations of the leveling network.

Utkan Mustafa Durdağ

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-210', Anonymous Referee #1, 15 Dec 2023
    • AC4: 'Reply on RC1', utkan mustafa durdag, 30 Dec 2023
      • RC4: 'Reply on AC4', Anonymous Referee #1, 02 Jan 2024
  • CC1: 'Comment on gmd-2023-210', Xinyue Yang, 19 Dec 2023
    • AC2: 'Reply on CC1', utkan mustafa durdag, 26 Dec 2023
      • RC3: 'Reply on AC2', Anonymous Referee #1, 02 Jan 2024
  • CEC1: 'Comment on gmd-2023-210', Juan Antonio Añel, 20 Dec 2023
    • AC1: 'Reply on CEC1', utkan mustafa durdag, 21 Dec 2023
  • RC2: 'Comment on gmd-2023-210', Anonymous Referee #2, 23 Dec 2023
    • AC3: 'Reply on RC2', utkan mustafa durdag, 27 Dec 2023
Utkan Mustafa Durdağ

Model code and software

Model Utkan Mustafa Durdag

Utkan Mustafa Durdağ


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Short summary
This study introduces a novel approach to outlier detection in geodetic networks, challenging conventional and robust methods. By treating outliers as unknown parameters within the Gauss-Markov Model and exploring numerous outlier combinations, this approach prioritizes minimal variance and eliminates iteration dependencies. The Mean Success Rate (MSR) comparisons highlight its effectiveness, improving MSR by 40–45 % for multiple outliers.