Knowledge-guided Gross Errors Detection and Elimination Approach of Landslide Monitoring Data

In order to avoid the disaster misjudgment and incorrect reports of landslide disaster, the detection and elimination of the gross errors of landslide monitoring data, has become a critical issue for the observational data quality control. The traditional data filtering methods using curve characteristics of single data source, which are limited by the characteristic of mutations-smooth relations and it is also hard to effectively distinguish the gross error and singular value induced by external factors. To overcome these problems, an approach for gross errors detection and elimination guided by landslide knowledge is proposed in this paper. Experimental results prove that more accurate and reliable landslide deformation information can be available. And proposed method can improve the automation and intelligent level of the gross errors detection and elimination for landslide monitoring data.