Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be
文献类型：期刊论文
| 作者 | Fang, Zhice1,2; Wang, Yi1,3; van Westen, Cees2; Lombardo, Luigi2 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2024-02-01 |
| 卷号 | 126页码:13 |
| 关键词 | Spatiotemporal landslide hazard prediction Exceedance probability Slope unit Spatiotemporal cross-validation |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2023.103631 |
| 通讯作者 | Wang, Yi(cug.yi.wang@gmail.com) |
| 英文摘要 | The geoscientific community primarily focuses on predicting where landslides are likely to occur through datadriven susceptibility models. Recently, few researchers have turned to statistical estimation of landslide planimetric area within a given terrain unit and exploration of the spatiotemporal distribution of landslide occurrence. However, these data-driven approaches cannot fulfill the commonly accepted definition of landslide hazard and cannot predict the location, time/frequency, and magnitude of landslide occurrence simultaneously. This study proposes a unified and data-driven framework for landslide hazard spatiotemporal modeling, enabling dynamic and probabilistic estimation of landslide occurrence within a given slope unit at a specific time period and for a specific landslide magnitude. This framework not only involves static and dynamic factors in modelling, but also considers spatial and temporal interactions to explore the spatiotemporal variation effects of landslide hazard. We test this framework on the main island of Taiwan with a multi-temporal landslide inventory from 2004 to 2018. Specifically, this framework assumes that the occurrence and size of landslides spatiotemporally follows a binomial and a Log-Gaussian distribution, respectively, and then uses generalized additive models to achieve the estimation of landslide hazard probability. Finally, the performance is validated by a spatiotemporal leave-oneout cross-validation scheme. We believe that this framework will lay the foundation for the community to estimate landslide hazard in a unified and probabilistic data-driven prototype. We envision it could lead to studies of dynamic hazard responses to climate change. |
| WOS关键词 | SUSCEPTIBILITY ; INVENTORIES ; MACHINE ; TAIWAN ; AREAS ; SCALE ; BASIN |
| 资助项目 | Joint Fund of the National Natural Science Foundation of China[U21A2013] ; State Key Laboratory of Resources and Environmental Information System ; China Scholarship Council[202106410043] ; Fundamental Research Funds for National Universities, China University of Geosciences (Wuhan) ; King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia[URF/1/4338-01-01] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001149732200001 |
| 出版者 | ELSEVIER |
| 资助机构 | Joint Fund of the National Natural Science Foundation of China ; State Key Laboratory of Resources and Environmental Information System ; China Scholarship Council ; Fundamental Research Funds for National Universities, China University of Geosciences (Wuhan) ; King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/202417]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Yi |
| 作者单位 | 1.China Univ Geosci, Sch Geophys & Geomat, Wuhan 430074, Peoples R China 2.Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, PO Box 217, NL-7500 AE Enschede, Netherlands 3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fang, Zhice,Wang, Yi,van Westen, Cees,et al. Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2024,126:13. |
| APA | Fang, Zhice,Wang, Yi,van Westen, Cees,&Lombardo, Luigi.(2024).Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,126,13. |
| MLA | Fang, Zhice,et al."Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 126(2024):13. |
入库方式： OAI收割
来源：地理科学与资源研究所
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