Examining the Spatially Varying Relationships between Landslide Susceptibility and Conditioning Factors Using a Geographical Random Forest Approach: A Case Study in Liangshan, China
文献类型：期刊论文
| 作者 | Dai, Xiaoliang2; Zhu, Yunqiang3; Sun, Kai; Zou, Qiang1; Zhao, Shen4; Li, Weirong2; Hu, Lei2; Wang, Shu |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-03-01 |
| 卷号 | 15期号:6页码:1513 |
| 关键词 | landslide susceptibility geographical random forest spatial heterogeneity local feature importance spatial cross validation |
| DOI | 10.3390/rs15061513 |
| 文献子类 | Article |
| 英文摘要 | Landslide susceptibility assessment is an important means of helping to reduce and manage landslide risk. The existing studies, however, fail to examine the spatially varying relationships between landslide susceptibility and its explanatory factors. This paper investigates the spatial variation in such relationships in Liangshan, China, leveraging a spatially explicit model, namely, geographical random forest (GRF). By comparing with random forest (RF), we found that GRF achieves a higher performance with an AUC of 0.86 due to its consideration of the spatial heterogeneity among variables. GRF also provides a higher-quality landslide susceptibility map than RF by correctly placing 92.35% of the landslide points in high-susceptibility areas. The local feature importance derived from GRF allows us to understand that the impact of conditioning factors varies across space, which can provide implications for policy development by local governments to place different levels of attention on different conditioning factors in specific counties to prevent and mitigate landslides. To account for the spatial dependence among the data in the model performance assessment, we use spatial cross-validation (CV) to split the data into subsets spatially rather than randomly for model training and testing. The results show that spatial CV can effectively address the over-optimistic bias in model error evaluation. |
| WOS关键词 | FREQUENCY RATIO ; NEURAL-NETWORK ; DECISION TREE ; PREDICTION ; MODELS ; DISPLACEMENT ; WEIGHT ; COUNTY |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:000958190300001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/190522]  |
| 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.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610299, Peoples R China 5.Minist Housing & Urban Rural Dev, Policy Res Ctr, Beijing 100835, Peoples R China |
| 推荐引用方式 GB/T 7714 | Dai, Xiaoliang,Zhu, Yunqiang,Sun, Kai,et al. Examining the Spatially Varying Relationships between Landslide Susceptibility and Conditioning Factors Using a Geographical Random Forest Approach: A Case Study in Liangshan, China[J]. REMOTE SENSING,2023,15(6):1513. |
| APA | Dai, Xiaoliang.,Zhu, Yunqiang.,Sun, Kai.,Zou, Qiang.,Zhao, Shen.,...&Wang, Shu.(2023).Examining the Spatially Varying Relationships between Landslide Susceptibility and Conditioning Factors Using a Geographical Random Forest Approach: A Case Study in Liangshan, China.REMOTE SENSING,15(6),1513. |
| MLA | Dai, Xiaoliang,et al."Examining the Spatially Varying Relationships between Landslide Susceptibility and Conditioning Factors Using a Geographical Random Forest Approach: A Case Study in Liangshan, China".REMOTE SENSING 15.6(2023):1513. |
入库方式： OAI收割
来源：地理科学与资源研究所
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