globalchange  > 气候变化与战略
DOI: 10.1007/s11069-021-04731-8
论文题名:
Landslide inventory and susceptibility models considering the landslide typology using deep learning: Himalayas, India
作者: Bera S.; Upadhyay V.K.; Guru B.; Oommen T.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2021
卷: 108, 期:1
起始页码: 1257
结束页码: 1289
语种: 英语
中文关键词: Deep learning ; Kalimpong (Himalayas) ; Landslide inventory ; Landslide typology ; Spatial agreement
英文关键词: algorithm ; debris avalanche ; debris flow ; landslide ; machine learning ; mapping method ; spatiotemporal analysis ; typology ; Himalayas ; India
英文摘要: Landslide susceptibility modeling is complex as it involves different types of landslides and diverse interests of the end-user. Developing mitigation strategies for the landslides depends on their typology. Therefore, a landslide susceptibility based on different types should be more appealing than a susceptibility model based on a single inventory set. In this research, susceptibility models are generated considering the different types of landslides. Prior to the development of the model, we analyzed landslide inventory for understanding the complexity and scope of alternative landslide susceptibility mapping. We conducted this work by examining a case study of Kalimpong region (Himalayas), characterized by different types of landslides. The landslide inventory was analyzed based on its differential attributes, such as movement types, state of activity, material type, distribution, style, and failure mechanism. From the landslide inventory, debris slides, rockslides, and rockfalls were identified to generate two landslide susceptibility models using deep learning algorithms. The findings showed high accuracy for both models (above 0.90), although the spatial agreement is highly varied among the models. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/168911
Appears in Collections:气候变化与战略

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作者单位: Centre for Geoinformatics, Jamsetji Tata School of Disaster Studies, Tata Institute of Social Sciences, V.N. Purav Marg, Mumbai, 400088, India; Geoinformatics Division, Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, 208016, India; Department of Geology, School of Earth Sciences, Central University of Tamil Nadu, Thiruvarur, India; Department of Geological and Mining Engineering and Sciences, Michigan Technological University, 1400. Townsend Drive, Houghton, MI 49931, United States

Recommended Citation:
Bera S.,Upadhyay V.K.,Guru B.,et al. Landslide inventory and susceptibility models considering the landslide typology using deep learning: Himalayas, India[J]. Natural Hazards,2021-01-01,108(1)
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