DOI: 10.1007/s11069-020-03927-8
论文题名: Multi-geohazards susceptibility mapping based on machine learning—a case study in Jiuzhaigou, China
作者: Cao J. ; Zhang Z. ; Du J. ; Zhang L. ; Song Y. ; Sun G.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2020
卷: 102, 期: 3 起始页码: 851
结束页码: 871
语种: 英语
中文关键词: 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
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. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/168416
Appears in Collections: 气候变化与战略
There are no files associated with this item.
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
Recommended Citation:
Cao J.,Zhang Z.,Du J.,et al. Multi-geohazards susceptibility mapping based on machine learning—a case study in Jiuzhaigou, China[J]. Natural Hazards,2020-01-01,102(3)