ZHU Qing, MIAO Shuangxi, DING Yulin, QI Hua, HE Xiaobo, CAO Zhenyu. Knowledge-guided Gross Errors Detection and Elimination Approach of Landslide Monitoring Data[J]. Geomatics and Information Science of Wuhan University, 2017, 42(4): 496-502. DOI: 10.13203/j.whugis20150125
Citation: ZHU Qing, MIAO Shuangxi, DING Yulin, QI Hua, HE Xiaobo, CAO Zhenyu. Knowledge-guided Gross Errors Detection and Elimination Approach of Landslide Monitoring Data[J]. Geomatics and Information Science of Wuhan University, 2017, 42(4): 496-502. DOI: 10.13203/j.whugis20150125

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.
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