气候变化领域集成服务门户(CCPortal): Landslide inventory and susceptibility models considering the landslide typology using deep learning: Himalayas, India

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DOI	10.1007/s11069-021-04731-8
	Landslide inventory and susceptibility models considering the landslide typology using deep learning: Himalayas, India
	Bera S.; Upadhyay V.K.; Guru B.; Oommen T.
发表日期	2021
ISSN	0921030X
起始页码	1257
结束页码	1289
卷号	108 期号:1
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.
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
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/206112
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
推荐引用方式 GB/T 7714	Bera S.,Upadhyay V.K.,Guru B.,et al. Landslide inventory and susceptibility models considering the landslide typology using deep learning: Himalayas, India[J],2021,108(1).
APA	Bera S.,Upadhyay V.K.,Guru B.,&Oommen T..(2021).Landslide inventory and susceptibility models considering the landslide typology using deep learning: Himalayas, India.Natural Hazards,108(1).
MLA	Bera S.,et al."Landslide inventory and susceptibility models considering the landslide typology using deep learning: Himalayas, India".Natural Hazards 108.1(2021).