Landslide spatial prediction using cluster analysis
文献类型：期刊论文
| 作者 | Zhao, Zheng2; Lan, Hengxing1,3; Li, Langping2; Strom, Alexander4 |
| 刊名 | GONDWANA RESEARCH
 |
| 出版日期 | 2024-06-01 |
| 卷号 | 130页码:291-307 |
| 关键词 | Landslide Spatial prediction Susceptibility Temporal clustering |
| DOI | 10.1016/j.gr.2024.02.006 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Temporal clustering is an intrinsic nature of landslide occurrences, therefore it should be considered in data-driven landslide spatial prediction (i.e., susceptibility assessment). However, it remains problematic regarding how to determine landslide temporal clusters and how to integrate susceptibility maps derived from different landslide temporal clusters. In this paper, a general framework of landslide spatial prediction model considering the temporal clustering of landslides is proposed. This novel framework first assesses landslide susceptibility separately based on each landslide temporal cluster identified by spatiotemporal clustering analysis and then integrates separate assessments by weighted averaging. In a case study, this general framework is implemented using the stacking network landslide susceptibility assessment method and used in the landslide spatial prediction of the Sanming City and Wenchuan seismic areas. The results show that the proposed framework outperformed traditional susceptibility models that do not consider landslide temporal clustering, and the integration of susceptibility models based on all landslide temporal clusters will promote the performance of landslide spatial prediction because levels of knowledge in long-term spatiotemporal landslide activities are considered. This novel general framework highlights the benefit of considering landslide temporal clustering in landslide spatial prediction and can provide better support for landslide risk management. (c) 2024 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved. |
| WOS关键词 | WENCHUAN EARTHQUAKE ; PATH DEPENDENCY ; SUSCEPTIBILITY ASSESSMENT ; NEURAL-NETWORKS ; AREA ; ENSEMBLES ; SELECTION ; CLIMATE ; EVENTS ; FOREST |
| WOS研究方向 | Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001202338900001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/204817]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Changan Univ, Sch Geol Engn & Geomatics, Xian 710064, Peoples R China 3.Univ Chinese Acad Sci, Beijing 10049, Peoples R China 4.Changan Univ, Key Lab Ecol Geol & Disaster Prevent, Minist Nat Resources, Xian 710064, Peoples R China 5.JSC Hydroproject Inst, Moscow 125993, Russia |
| 推荐引用方式 GB/T 7714 | Zhao, Zheng,Lan, Hengxing,Li, Langping,et al. Landslide spatial prediction using cluster analysis[J]. GONDWANA RESEARCH,2024,130:291-307. |
| APA | Zhao, Zheng,Lan, Hengxing,Li, Langping,&Strom, Alexander.(2024).Landslide spatial prediction using cluster analysis.GONDWANA RESEARCH,130,291-307. |
| MLA | Zhao, Zheng,et al."Landslide spatial prediction using cluster analysis".GONDWANA RESEARCH 130(2024):291-307. |
入库方式： OAI收割
来源：地理科学与资源研究所
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