Spatiotemporal deformation characteristics of Outang landslide and identification of triggering factors using data mining

• DOI: 10.1016/j.jrmge.2023.09.030
• Publication Date: 2024-01-21T08:16:09Z
• Authors: Beibei Yang; Zhongqiang Liu; Suzanne Lacasse; Xin Liang
• Keywords: Deformation monitoring

Since the impoundment of Three Gorges Reservoir (TGR) in 2003, numerous slopes have experienced noticeable movement or destabilization owing to reservoir level changes and seasonal rainfall. One case is Outang landslide, a large-scale active on south bank Yangtze River. The latest monitoring data site investigations available are analyzed establish spatial temporal landslide deformation characteristics. Data mining technology, including two-step clustering Apriori algorithm, then used identify dominant triggers movement. In process, method clusters candidate displacement rate into several groups, algorithm generates correlation criteria for cause-and-effect. analysis considers multiple locations incorporates two types time scales: long-term monthly basis short-term daily basis. This shows that deformations driven by both rainfall water while its varies spatiotemporally mainly due difference local responses hydrological factors. results reveal different triggering factors depending frequency: bi-monthly cumulative control deformation, 10-d 5-d drop dominate landslide. It concluded spatiotemporal pattern rules associated with precipitation potential be broadly implemented improving prevention dam reservoirs other landslide-prone areas.