Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine

doi:10.3390/app11041381

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所 > 山地灾害与地表过程重点实验室

	Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine
	Li, Xiuzhen 2,3; Li, Shengwei 1
刊名	APPLIED SCIENCES-BASEL
	2021-02-01
卷号	11 期号:4 页码:14
关键词	large-scale landslide displacement rate prediction multi-factor support vector regression machine
ISSN号	无
DOI	10.3390/app11041381
英文摘要	Forecasting the development of large-scale landslides is a contentious and complicated issue. In this study, we put forward the use of multi-factor support vector regression machines (SVRMs) for predicting the displacement rate of a large-scale landslide. The relative relationships between the main monitoring factors were analyzed based on the long-term monitoring data of the landslide and the grey correlation analysis theory. We found that the average correlation between landslide displacement and rainfall is 0.894, and the correlation between landslide displacement and reservoir water level is 0.338. Finally, based on an in-depth analysis of the basic characteristics, influencing factors, and development of landslides, three main factors (i.e., the displacement rate, reservoir water level, and rainfall) were selected to build single-factor, two-factor, and three-factor SVRM models. The key parameters of the models were determined using a grid-search method, and the models showed high accuracies. Moreover, the accuracy of the two-factor SVRM model (displacement rate and rainfall) is the highest with the smallest standard error (RMSE) of 0.00614; it is followed by the three-factor and single-factor SVRM models, the latter of which has the lowest prediction accuracy, with the largest RMSE of 0.01644.
资助项目	National Natural Science Foundation of China[41772386] ; Strategic Leading Science and Technology Project of Chinese Academy of Sciences[XDA23090203] ; National Key Research and Development Plan of China[YS2018YFGH000001]
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
语种	英语
出版者	MDPI
WOS记录号	WOS:000632109300001
资助机构	National Natural Science Foundation of China ; Strategic Leading Science and Technology Project of Chinese Academy of Sciences ; National Key Research and Development Plan of China
内容类型	期刊论文
源URL	[http://ir.imde.ac.cn/handle/131551/56254]
专题	成都山地灾害与环境研究所_山地灾害与地表过程重点实验室
通讯作者	Li, Xiuzhen; Li, Shengwei
作者单位	1.China Geol Survey, Chengdu Ctr, Chengdu 610081, Peoples R China 2.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China 3.Chinese Acad Sci, Key Lab Mt Hazards & Earth Surface Proc, Chengdu 610041, Peoples R China
推荐引用方式 GB/T 7714	Li, Xiuzhen,Li, Shengwei. Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine[J]. APPLIED SCIENCES-BASEL,2021,11(4):14.
APA	Li, Xiuzhen,&Li, Shengwei.(2021).Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine.APPLIED SCIENCES-BASEL,11(4),14.
MLA	Li, Xiuzhen,et al."Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine".APPLIED SCIENCES-BASEL 11.4(2021):14.