气候变化领域集成服务门户(CCPortal): Mapping the susceptibility to landslides based on the deep belief network: a case study in Sichuan Province, China

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DOI	10.1007/s11069-020-04128-z
	Mapping the susceptibility to landslides based on the deep belief network: a case study in Sichuan Province, China
	Wang W.; He Z.; Han Z.; Li Y.; Dou J.; Huang J.
发表日期	2020
ISSN	0921030X
起始页码	3239
结束页码	3261
卷号	103 期号:3
英文摘要	A dataset of landslides from Sichuan Province in China, containing 1551 historical individual landslides, is a result of two teams’ effort in the past few years to map the susceptibility to landslides. Considering complex internal relations among the triggering factors, logistic regression (LR) and shallow neural networks, such as back-propagation neural network (BPNN), are often limited. In this paper, we make a straightforward development that the deep belief network (DBN) based on deep learning technology is introduced to map the regional susceptibility to landslides. Seven factors with respect to geomorphology, geology and hydrology are considered and verified through the collinearity test. A DBN model containing three pre-trained layers of restricted Boltzmann machines by stochastic gradient descent method is configured to obtain the susceptibility to landslides. Susceptibility results evaluated by DBN model are compared with those by LR and BPNN in the receive operator characteristic (ROC) analysis, showing that DBN has a better prediction precision, with a lower rate of false alarms and fake alarms. The case study also indicates different sensitivities of the triggering factors to the landslide susceptibility, that the factors of altitude, distance to drainage network and average annual rainfall have significant impact in mapping the susceptibility to landslides in the region. This research will contribute to a better-performance model for regional-scale mapping for the susceptibility to landslides, in particular, at the area where triggering factors show complex relations and relative independence. © 2020, Springer Nature B.V.
关键词	Deep belief network Deep learning Landslides mapping Sichuan area Susceptibility
英文关键词	artificial neural network; back propagation; data set; geological mapping; landslide; machine learning; regression analysis; stochasticity; trigger mechanism; China; Sichuan
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/205987
作者单位	School of Civil Engineering, Central South University, 22 Shaoshan Road, Changsha, Hunan 410075, China; The Key Laboratory of Engineering Structures of Heavy Haul Railway, Ministry of Education, Changsha, 410075, China; Department of Civil and Environmental Engineering, Nagaoka University of Technology, Niigata, 940-2188, Japan
推荐引用方式 GB/T 7714	Wang W.,He Z.,Han Z.,et al. Mapping the susceptibility to landslides based on the deep belief network: a case study in Sichuan Province, China[J],2020,103(3).
APA	Wang W.,He Z.,Han Z.,Li Y.,Dou J.,&Huang J..(2020).Mapping the susceptibility to landslides based on the deep belief network: a case study in Sichuan Province, China.Natural Hazards,103(3).
MLA	Wang W.,et al."Mapping the susceptibility to landslides based on the deep belief network: a case study in Sichuan Province, China".Natural Hazards 103.3(2020).