DOI: 10.1007/s11069-021-04844-0
论文题名: A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping
作者: Wei X. ; Zhang L. ; Luo J. ; Liu D.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2021
语种: 英语
中文关键词: Convolutional neural network
; Generalization ability
; Hybrid model
; Landslide susceptibility mapping
; TRIGRS
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. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/169457
Appears in Collections: 气候变化与战略
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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
Recommended Citation:
Wei X.,Zhang L.,Luo J.,et al. A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping[J]. Natural Hazards,2021-01-01