气候变化领域集成服务门户(CCPortal): Triggering factors and threshold analysis of baishuihe landslide based on the data mining methods

CCPortal

DOI	10.1007/s11069-020-04419-5
	Triggering factors and threshold analysis of baishuihe landslide based on the data mining methods
	Miao F.; Wu Y.; Li L.; Liao K.; Xue Y.
发表日期	2021
ISSN	0921030X
起始页码	2677
结束页码	2696
卷号	105 期号:3
英文摘要	The analysis of landslide monitoring data is important to the study and prediction of landslide deformation but is very challenging. In this research, a data mining method combining two-step clustering, Apriori algorithm and decision tree C5.0 model are proposed, and the Baishuihe Landslide in the Three Gorges Reservoir area is taken as the study case. 6 hydrologic factors related to rainfall and reservoir water level are chosen to carry out the data mining analysis. First, 6 hydrologic triggering factors and the deformation rate of the landslide are clustered by the two-step clustering. Then, the Apriori algorithm is used to mine the association rules between triggering factors and deformation rate. A total of 173 association rules are generated based on the data mining, and 20 rules are selected to be analyzed. At last, the decision tree C5.0 model is built to carry out threshold analysis of hydrologic triggering factors. The results show that monthly cumulative rainfall plays an important role in controlling landslide deformation, and 73.9 mm can be regarded as its threshold. Monthly average water level is the second factor to control landslide deformation. While the monthly maximum daily rainfall has no direct control over the acceleration stage of landslide deformation. The data mining method proposed in this paper has a high accuracy in the study of Baishuihe landslide, which could provide a significant basis for the data analysis and prediction of the accumulative landslide in the Three Gorges Reservoir area. © 2020, Springer Nature B.V.
关键词	Apriori algorithm Baishuihe landslide Data mining Decision tree C5.0 Three gorges reservoir Two-step clustering
英文关键词	algorithm; computer simulation; data mining; flow modeling; hydrological modeling; landslide; numerical model; rainfall-runoff modeling; water level; China; Three Gorges Reservoir
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206427
作者单位	Faculty of Engineering, China University of Geosciences, Wuhan, 430074, China
推荐引用方式 GB/T 7714	Miao F.,Wu Y.,Li L.,et al. Triggering factors and threshold analysis of baishuihe landslide based on the data mining methods[J],2021,105(3).
APA	Miao F.,Wu Y.,Li L.,Liao K.,&Xue Y..(2021).Triggering factors and threshold analysis of baishuihe landslide based on the data mining methods.Natural Hazards,105(3).
MLA	Miao F.,et al."Triggering factors and threshold analysis of baishuihe landslide based on the data mining methods".Natural Hazards 105.3(2021).