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Debris-Flow Susceptibility Assessment in China: A Comparison between Traditional Statistical and Machine Learning Methods

文献类型：期刊论文

作者Huang, Han2,3; Wang, Yongsheng2; Li, Yamei3,4; Zhou, Yang1; Zeng, Zhaoqi3,5
刊名REMOTE SENSING
出版日期2022-09-01
卷号14期号:18页码:20
关键词debris-flow susceptibility information value logistic regression random forest comparative evaluation China
DOI10.3390/rs14184475
通讯作者Zhou, Yang(zhouyang2021@ruc.edu.cn)
英文摘要Debris flows, triggered by dual interferences extrinsically and intrinsically, have been widespread in China. The debris-flow susceptibility (DFS) assessment is acknowledged as the benchmark for the mitigation and prevention of debris flow risks, but DFS assessments at the national level are lacking. The role of human activities in the DFS assessment has always been overlooked. On the basis of a detailed inventory of debris-flow sites and a large set of environmental and human-related characteristics, this research presents the comparative performance of the well-known information value (IV), logistic regression (LR) and random forest (RF) models for DFS assessments in China. Twelve causative factors, namely, elevation, slope, aspect, rainfall, the normalized difference vegetation index (NDVI), land use, landform, geology, distance to faults, density of villages, distance to rivers and distance to roads, were considered. Debris-flow susceptibility maps were then generated after the nonlinear relationship between the debris-flow occurrence and the causative factors was captured. Finally, the predictive performance of the three maps was evaluated through receiver operating characteristic (ROC) curves, and the validation results showed that areas under the ROC curves were 81.98%, 79.96% and 97.38% for the IV, LR and RF models, respectively, indicating that the RF model outperformed the other two traditional statistical methods. The importance ranking of the RF model also revealed that distance to roads, slope and rainfall dominated the spatial distribution of debris flows. This is the first experiment to compare between the traditional statistical and machine learning methods in DFS studies for the whole of China. Our results could provide some empirical support for China's policymakers and local practitioners in their efforts to enable residents to be less vulnerable to disasters.
WOS关键词INFORMATION VALUE METHOD ; LANDSLIDE SUSCEPTIBILITY ; LOGISTIC-REGRESSION ; SPATIAL PREDICTION ; VALUE MODEL ; GIS ; AREA ; PROVINCE ; REGION ; INITIATION
资助项目Strategic Priority Research Program of Chinese Academy of Sciences[XDA20090000] ; National Natural Science Foundation of China[41871183] ; Strategic Priority Research Program of Chinese Academy of Sciences, Pan-Third Pole Environment Study for a Green Silk Road (Pan-TPE)[XDA20090000]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者MDPI
WOS记录号WOS:000856759900001
资助机构Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Sciences, Pan-Third Pole Environment Study for a Green Silk Road (Pan-TPE)
源URL[http://ir.igsnrr.ac.cn/handle/311030/184807]  
专题中国科学院地理科学与资源研究所
通讯作者Zhou, Yang
作者单位1.Renmin Univ China, Sch Agr Econ & Rural Dev, Beijing 100872, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Reg Sustainable Dev Modeling, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Dept Environm & Resources, Beijing 100049, Peoples R China
4.Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Huang, Han,Wang, Yongsheng,Li, Yamei,et al. Debris-Flow Susceptibility Assessment in China: A Comparison between Traditional Statistical and Machine Learning Methods[J]. REMOTE SENSING,2022,14(18):20.
APA Huang, Han,Wang, Yongsheng,Li, Yamei,Zhou, Yang,&Zeng, Zhaoqi.(2022).Debris-Flow Susceptibility Assessment in China: A Comparison between Traditional Statistical and Machine Learning Methods.REMOTE SENSING,14(18),20.
MLA Huang, Han,et al."Debris-Flow Susceptibility Assessment in China: A Comparison between Traditional Statistical and Machine Learning Methods".REMOTE SENSING 14.18(2022):20.

入库方式： OAI收割

来源：地理科学与资源研究所

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