Spatial Prediction of Landslide Susceptibility Using a Deep Learning and Partition Membership Hybrid Model
文献类型：期刊论文
| 作者 | Lin, Qigen2; Hong, Haoyuan1,2 |
| 刊名 | TRANSACTIONS IN GIS
 |
| 出版日期 | 2026-04-20 |
| 卷号 | 30期号:2页码:e70255 |
| 关键词 | Deep learning Hybrid model Landslide susceptibility Unbalanced data |
| ISSN号 | 1361-1682 |
| DOI | 10.1111/tgis.70255 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | The spatial prediction of landslide susceptibility (LS) is a very important tool for preventing losses caused by landslide disasters. Deep learning is a state-of-the-art method that has been used to predict the spatial distribution of LS, but the influence of combining deep learning technology with a partition membership model and then applying the hybrid model to the spatial prediction of LS is still an important research question and challenge. The aim of this study is to design a hybrid model combining deep learning (DL) with partition membership (PM) for modeling LS. To validate our hybrid model method, Zixi County, which is located in Jiangxi Province, China, was selected as the experimental area. On the basis of the landslide inventory map, 233 landslide locations were identified, and fifteen environmental factors were analyzed on the basis of expert knowledge. To assess the effectiveness and superiority of the proposed PMDL model, the support vector machine (SVM), Hoeffding tree (HT), Naive Bayes (NB) and stochastic gradient descent (SGD) methods were selected as representative traditional models, and they were compared with the proposed PMDL model on the basis of the index of the AUC value. The results indicate that the designed PMDL hybrid model is more reliable and stable. The effectiveness of predicting landslide susceptibility is confirmed by the proposed hybrid model. |
| URL标识 | 查看原文 |
| WOS关键词 | SUPPORT VECTOR MACHINE ; LOGISTIC-REGRESSION ; OPTIMIZATION ; UNCERTAINTY ; SELECTION |
| WOS研究方向 | Geography |
| 语种 | 英语 |
| WOS记录号 | WOS:001743750700001 |
| 出版者 | WILEY |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/221531]  |
| 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 | Hong, Haoyuan |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 2.Nanjing Univ Informat Sci & Technol, Sch Geog Sci, Nanjing, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Lin, Qigen,Hong, Haoyuan. Spatial Prediction of Landslide Susceptibility Using a Deep Learning and Partition Membership Hybrid Model[J]. TRANSACTIONS IN GIS,2026,30(2):e70255. |
| APA | Lin, Qigen,&Hong, Haoyuan.(2026).Spatial Prediction of Landslide Susceptibility Using a Deep Learning and Partition Membership Hybrid Model.TRANSACTIONS IN GIS,30(2),e70255. |
| MLA | Lin, Qigen,et al."Spatial Prediction of Landslide Susceptibility Using a Deep Learning and Partition Membership Hybrid Model".TRANSACTIONS IN GIS 30.2(2026):e70255. |
入库方式： OAI收割
来源：地理科学与资源研究所
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