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. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping

文献类型：期刊论文

作者Liu, Rui2,3; Yang, Xin2,3; Xu, Chong4; Wei, Liangshuai5; Zeng, Xiangqiang1,6
刊名REMOTE SENSING
出版日期2022
卷号14期号:2页码:31
关键词landslide susceptibility mapping convolutional neural network machine learning GIS Jiuzhaigou region Lantau Island
DOI10.3390/rs14020321
通讯作者Yang, Xin(xinyang@stu.cdut.edu.cn)
英文摘要Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of landslide occurrence, providing a scientific basis for natural hazards prevention, land use planning, and economic development in landslide-prone areas. To date, a large number of machine learning methods have been applied to LSM, and recently the advanced convolutional neural network (CNN) has been gradually adopted to enhance the prediction accuracy of LSM. The objective of this study is to introduce a CNN-based model in LSM and systematically compare its overall performance with the conventional machine learning models of random forest, logistic regression, and support vector machine. Herein, we selected Zhangzha Town in Sichuan Province, China, and Lantau Island in Hong Kong, China, as the study areas. Each landslide inventory and corresponding predisposing factors were stacked to form spatial datasets for LSM. The receiver operating characteristic analysis, area under the curve (AUC), and several statistical metrics, such as accuracy, root mean square error, Kappa coefficient, sensitivity, and specificity, were used to evaluate the performance of the models. Finally, the trained models were calculated, and the landslide susceptibility zones were mapped. Results suggest that both CNN and conventional machine learning-based models have a satisfactory performance. The CNN-based model exhibits an excellent prediction capability and achieves the highest performance but also significantly reduces the salt-of-pepper effect, which indicates its great potential for application to LSM.
WOS关键词LOGISTIC-REGRESSION ; FREQUENCY RATIO ; DISCRIMINANT-ANALYSIS ; 3 GORGES ; CLASSIFICATION ; EARTHQUAKE ; ENTROPY ; INDEX ; AGREEMENT ; WEIGHTS
资助项目Bureau of Geology and mineral resources exploration and development of Sichuan Province[20170612-0413] ; The Research Center for Meteorological Disaster Prediction and Early Warning and Emergency Management 2021 General Project[ZHYJ21-YB04]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000746357600001
出版者MDPI
资助机构Bureau of Geology and mineral resources exploration and development of Sichuan Province ; The Research Center for Meteorological Disaster Prediction and Early Warning and Emergency Management 2021 General Project
源URL[http://ir.igsnrr.ac.cn/handle/311030/169959]  
专题中国科学院地理科学与资源研究所
通讯作者Yang, Xin
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chengdu Univ Technol, Coll Geophys, Chengdu 610059, Peoples R China
3.Chengdu Univ Technol, Key Lab Earth Explorat & Informat Technol, Minist Educ, Chengdu 610059, Peoples R China
4.Minist Emergency Management China, Natl Inst Nat Hazards, Beijing 100085, Peoples R China
5.Chinese Acad Geol Sci, Inst Explorat Technol, Chengdu 611734, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Rui,Yang, Xin,Xu, Chong,et al. Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping[J]. REMOTE SENSING,2022,14(2):31.
APA Liu, Rui,Yang, Xin,Xu, Chong,Wei, Liangshuai,&Zeng, Xiangqiang.(2022).Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping.REMOTE SENSING,14(2),31.
MLA Liu, Rui,et al."Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping".REMOTE SENSING 14.2(2022):31.

入库方式： OAI收割

来源：地理科学与资源研究所

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。