Multi-sensor fusion of data for monitoring of Huangtupo landslide in the three Gorges Reservoir (China)

• Main Authors: Junqi Liu, Huiming Tang, Qi Li, Aijun Su, Qianhui Liu, Cheng Zhong
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2018-01-01
• Series: Geomatics, Natural Hazards & Risk
• Subjects: Landslide monitoring; Big Data; mathematical statistics; neural network; data fusion; the Three Gorges Reservoir
• Online Access: http://dx.doi.org/10.1080/19475705.2018.1478892

There hides a certain relationship among various monitoring data in a landslide, and the mining of this relationship is of significance to landslide research. In this paper, we first collect multiple monitoring data of riverside 1# slump-mass of Huangtupo landslide, the Three Gorges Reservoir Region, China, including Global Positioning System (GPS) monitoring data, inclinometer data, reservoir water level, rainfall, water content, crack width, groundwater level and temperature data, etc. By adopting the combination of quantitative statistics and qualitative simulation method for multi-sensor fusion monitoring data analysis, we overcome the one-sidedness of using a single method or single data type. The result of fusion analysis has indicated that in time periods with low rainfall or when the rainfall is not the major factor, main factors affecting landslide movement are crack development, water content of the landslide and water level of the Three Gorges Reservoir. Compared with the actual monitoring data, the fusion analysis results has a maximum error of 1.9%, which shows a good effect.