文献类型：期刊文献

英文题名：Novel Intelligent Approach for the Early Warning of Rainfall-Type Landslides Based on the BRB Model

作者：Huang, Man[1,2];Weng, Hanqian[2];Hong, Chenjie[3];Xu, Xiaobin[4];Tao, Zhigang[3];Li, Changhong[5];Huang, Yixiao[6]

机构：[1]Shaoxing Univ, 508 Huancheng West Rd, Shaoxing 312000, Peoples R China;[2]Shaoxing Univ, Dept Civil Engn, 508 Huancheng West Rd, Shaoxing 312000, Peoples R China;[3]State Key Lab Geomech & Deep Underground Engn, 11 Xueyuan Rd, Beijing 100083, Peoples R China;[4]Hangzhou Dianzi Univ, Sch Automat, 1158,2 Baiyang St, Hangzhou 310018, Peoples R China;[5]Zhejiang Bur Nonferrous Met Geol Explorat, 160 Renmin Zhong Lu, Shaoxing 312000, Peoples R China;[6]Zhejiang Univ Sci & Technol, Inst Geotech Engn, 318 Liuhe Rd, Hangzhou 310023, Peoples R China

年份：2022

卷号：22

期号：10

外文期刊名：INTERNATIONAL JOURNAL OF GEOMECHANICS

收录：SCI-EXPANDED(收录号：WOS:000840506600034)、、EI(收录号：20223112463626)、Scopus(收录号：2-s2.0-85135034328)、WOS

基金：This study has been partially funded by the Key Research and Development Projects of Zhejiang Province (No. 2019C03104).

语种：英文

外文关键词：Early warning of landslide; BRB model; Surface displacement; Rainfall

外文摘要：This work attempts to apply belief rule-based (BRB) model in information fusion method to landslides, to improve the accuracy and efficiency for early warning of landslides. Taking a typical rainfall-type landslide as the experimental area, the monitoring results find that the surface displacement is the most sensitive monitoring data. It is determined that the monitoring data of surface displacement change rate and rainfall intensity could be used as the input parameter of the BRB model. An initial BRB model is established by setting up the rule base for discriminating warning levels. The data from three monitoring points are collected for the optimization of the initial BRB model, and verification of the optimized BRB models. Results shows the optimized BRB model can accurately describe the nonlinear relationship between the selected monitoring data and the warning level, which provides an intelligent method for landslide prevention and has a strong application prospect.