Space-time modeling of landslide size by combining static, dynamic, and unobserved spatiotemporal factors
文献类型：期刊论文
| 作者 | Fang, Zhice1,4; Wang, Yi2,3,4; van Westen, Cees1; Lombardo, Luigi1 |
| 刊名 | CATENA
 |
| 出版日期 | 2024-05-01 |
| 卷号 | 240页码:107989 |
| 关键词 | Dynamic landslide area prediction Space-time modeling Slope unit Spatio-temporal cross-validation |
| DOI | 10.1016/j.catena.2024.107989 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | Landslide spatial prediction using data-driven models has predominantly concentrated on predicting where landslides may occur. Nevertheless, few researchers have turned to jointly modeling how large and when landslides will be for a given terrain unit. This study proposes a data-driven model capable of estimating how large landslides may be, for the entire Taiwan main island in a fourteen-year time window. To address this task, we implement a space-time generalized additive model to fit the complex relationships between environmental factors and landslide size. In addition to incorporating static and dynamic covariates into the modeling process, the model takes into account spatial and temporal interactions to elucidate the spatiotemporal variations affecting landslide size. To test the effectiveness of the model, we employ a comprehensive set of cross-validation (CV) procedures, includes a randomized 10fold-CV, a spatially constrained CV, a temporal leave-one-year-out CV, and a spatio-temporal CV. The experimental results demonstrate that the space-time model delivers acceptable and interpretable prediction outcomes, demonstrating the ability to predict landslide area for a given slope unit within a specified time period. We believe that the space-time landslide modeling will lay the foundation for landslide community to analyze landslide characteristic within a dynamic context. Furthermore, given its inherent spatio-temporal nature, we anticipate that this approach may pave the way for simulation studies exploring diverse climate scenarios. |
| WOS关键词 | PATH DEPENDENCY ; SUSCEPTIBILITY ; HAZARD ; INVENTORIES ; INSIGHTS ; FLOWS ; BASIN ; V1.0 |
| WOS研究方向 | Geology ; Agriculture ; Water Resources |
| WOS记录号 | WOS:001219760900001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/205203]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Wang, Yi |
| 作者单位 | 1.Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, POB 217, NL-7500 AE Enschede, Netherlands 2.State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Minist Nat Resources, Key Lab Ocean Space Resource Management Technol, Hangzhou 310012, Peoples R China 4.China Univ Geosci, Sch Geophys & Geomat, Wuhan 430074, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fang, Zhice,Wang, Yi,van Westen, Cees,et al. Space-time modeling of landslide size by combining static, dynamic, and unobserved spatiotemporal factors[J]. CATENA,2024,240:107989. |
| APA | Fang, Zhice,Wang, Yi,van Westen, Cees,&Lombardo, Luigi.(2024).Space-time modeling of landslide size by combining static, dynamic, and unobserved spatiotemporal factors.CATENA,240,107989. |
| MLA | Fang, Zhice,et al."Space-time modeling of landslide size by combining static, dynamic, and unobserved spatiotemporal factors".CATENA 240(2024):107989. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。