中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 成都山地灾害与环境研究所
  3. 中国科学院水利部成都山地灾害与环境研究所
  4. 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

文献类型：期刊论文

作者Li, Xiuzhen2,3; Li, Shengwei1
刊名APPLIED SCIENCES-BASEL
出版日期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
DOI10.3390/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
WOS记录号WOS:000632109300001
出版者MDPI
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
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.

入库方式： OAI收割

来源：成都山地灾害与环境研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。