气候变化领域集成服务门户(CCPortal): Multi-geohazards susceptibility mapping based on machine learning

CCPortal

DOI	10.1007/s11069-020-03927-8
	Multi-geohazards susceptibility mapping based on machine learning—a case study in Jiuzhaigou, China
	Cao J.; Zhang Z.; Du J.; Zhang L.; Song Y.; Sun G.
发表日期	2020
ISSN	0921030X
起始页码	851
结束页码	871
卷号	102 期号:3
英文摘要	Jiuzhaigou, located in the transitional area between the Qinghai–Tibet Plateau and the Sichuan Basin, is highly prone to geological hazards (e.g., rock fall, landslide, and debris flow). High-performance-based hazard prediction models, therefore, are urgently required to prevent related hazards and manage potential emergencies. Current researches mainly focus on susceptibility of single hazard but ignore that different types of geological hazards might occur simultaneously under a complex environment. Here, we firstly built a multi-geohazard inventory from 2000 to 2015 based on a geographical information system and used satellite data in Google earth and then chose twelve conditioning factors and three machine learning methods—random forest, support vector machine, and extreme gradient boosting (XGBoost)—to generate rock fall, landslide, and debris flow susceptibility maps. The results show that debris flow models presented the best prediction capabilities [area under the receiver operating characteristic curve (AUC 0.95)], followed by rock fall (AUC 0.94) and landslide (AUC 0.85). Additionally, XGBoost outperformed the other two methods with the highest AUC of 0.93. All three methods with AUC values larger than 0.84 suggest that these models have fairly good performance to assess geological hazards susceptibility. Finally, evolution index was constructed based on a joint probability of these three hazard models to predict the evolution tendency of 35 unstable slopes in Jiuzhaigou. The results show that these unstable slopes are likely to evolve into debris flows with a probability of 46%, followed by landslides (43%) and rock falls (29%). Higher susceptibility areas for geohazards were mainly located in the southeast and middle of Jiuzhaigou, implying geohazards prevention and mitigation measures should be taken there in near future. © 2020, Springer Nature B.V.
关键词	Evolution tendency Geological hazards Jiuzhaigou Machine learning Susceptibility
英文关键词	debris flow; geological mapping; GIS; landslide; machine learning; rockfall; satellite data; slope stability; China; Qinghai-Xizang Plateau; Sichuan Basin
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/205617
作者单位	State Key Laboratory of Earth Surface Processes and Resource Ecology/MEM&MoE, Key Laboratory of Environmental Change and Natural Hazards, Beijing Normal University, Beijing, 100875, China; Administration of Jiuzhaigou National Park of China, Zhangzha, China; Sichuan Geological and Mineral Bureau Regional Geological Survey Team, Chengdu, China; China-Croatia “Belt and Road” Joint Laboratory on Biodiversity and Ecosystem Services, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
推荐引用方式 GB/T 7714	Cao J.,Zhang Z.,Du J.,等. Multi-geohazards susceptibility mapping based on machine learning—a case study in Jiuzhaigou, China[J],2020,102(3).
APA	Cao J.,Zhang Z.,Du J.,Zhang L.,Song Y.,&Sun G..(2020).Multi-geohazards susceptibility mapping based on machine learning—a case study in Jiuzhaigou, China.Natural Hazards,102(3).
MLA	Cao J.,et al."Multi-geohazards susceptibility mapping based on machine learning—a case study in Jiuzhaigou, China".Natural Hazards 102.3(2020).