气候变化领域集成服务门户(CCPortal): A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping

CCPortal

DOI	10.1007/s11069-021-04844-0
	A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping
	Wei X.; Zhang L.; Luo J.; Liu D.
发表日期	2021
ISSN	0921030X
英文摘要	Landslide susceptibility mapping (LSM) is critical for risk assessment and mitigation. Generalization ability and prediction uncertainty are the current challenges for LSM but have been rarely investigated. The generalization ability refers to the ability of trained models to assess the landslide susceptibility of new areas and make accurate predictions. The prediction uncertainty mainly comes from the possibility of wrongly selecting the unstable landslide samples as stable ones from incomplete landslide inventory. This paper proposes a hybrid model by integrating the convolutional neural network (CNN) with physical model transient rainfall infiltration and grid-based regional slope-stability analysis (TRIGRS) to address the challenges above by combining the advantages of the two approaches. CNN is the main structure of the hybrid model and serves as a binary classifier to capture the spatial and inter-channel correlation among landslide conditioning factors and landslide inventory. TRIGRS characterizes the differences among grids caused by lithology by converting originally spatially discrete and banded lithology information into spatially continuous safety factors (Fs) within a fixed range and pre-selects training samples to ensure the correctness of the selected non-landslide grids. Two towns (Zhuyuan and Qinglian) in Fengjie, Chongqing, China, are used as the study area. A landslide inventory and landslide conditioning factor maps with 30 m resolution consist of the database. The performance of CNN and the proposed hybrid model is compared using the receiver operating characteristic curve and relative landslide density index (R-index). The superiority of the hybrid model and the effect of pre-selection of training samples are investigated. The results reveal that the generalization ability is enhanced and the prediction uncertainty is reduced by the proposed hybrid model. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
关键词	Convolutional neural network Generalization ability Hybrid model Landslide susceptibility mapping TRIGRS
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/206658
作者单位	State Key Laboratory of Ocean Engineering, Department of Civil Engineering, Shanghai Jiao Tong University, Room B522, Mulan Building, 800 Dongchuan Road, Shanghai, 200240, China; Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration (CISSE), Shanghai, 200240, China; Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai, 200240, China; Chongqing Bureau of Geology and Mineral Resources, Chongqing, China
推荐引用方式 GB/T 7714	Wei X.,Zhang L.,Luo J.,et al. A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping[J],2021.
APA	Wei X.,Zhang L.,Luo J.,&Liu D..(2021).A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping.Natural Hazards.
MLA	Wei X.,et al."A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping".Natural Hazards (2021).