Outlier Detection of Slope Deformation Monitoring Data based on WMA-3σ

DOI

10.2991/978-94-6463-398-6_19How to use a DOI?

Keywords

Slope deformation; Monitoring data; Outlier detection; Weighted moving average; 3σ criterion

Abstract

The outliers in slope deformation monitoring data often contain important information. The influence of external environment, the failure of slope structure and the failure of monitoring instrument are the important reasons for the outliers. Rapid and accurate detection of outliers is not only the basic work of data analysis and calculation, but also an important measure to find out whether the slope is safe in time. Slope deformation monitoring data is time series. The short-term changes is smooth and stable with strong autocorrelation. In this paper, an adaptive weight calculation method was proposed for Weighted Moving Average (WMA) algorithm. The algorithm can estimate the measured data with high precision without being affected by outliers. Then, the difference sequence between the estimated data and the measured data was calculated, and the mirror processing was proposed for the difference sequence. In order to eliminate the asymmetric distribution of the difference sequence caused by the trend of the measured data. Finally, the outliers of the difference sequences after mirror processing were detected using the 3σ criterion. Thus the outlier detection of the measured data is realized. Through example analysis, WMA-3σ method can accurately detect outliers in the measured data. It has important reference significance for real-time and efficient outlier detection and intelligent data analysis.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Bin Li
AU  - Xingping Bai
AU  - Huanhuan Gao
AU  - Ting Liu
PY  - 2024
DA  - 2024/04/24
TI  - Outlier Detection of Slope Deformation Monitoring Data based on WMA-3σ
BT  - Proceedings of the 2023 5th International Conference on Hydraulic, Civil and Construction Engineering (HCCE 2023)
PB  - Atlantis Press
SP  - 189
EP  - 199
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-398-6_19
DO  - 10.2991/978-94-6463-398-6_19
ID  - Li2024
ER  -