The research on landslide spatial prediction method based on knowledge graph and representation learning: a case study of Anxi county, Fujian Province
文献类型：期刊论文
| 作者 | Jin, Xinlei5,6; Li, Daichao5,6; Wang, Shu3,4; Ye, Peng2; Gao, Jialiang5,6; Liu, Mingjiang1; Zeng, Zhenwei5,6; Wu, Sheng5,6 |
| 刊名 | BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
 |
| 出版日期 | 2026-03-27 |
| 卷号 | 85期号:4页码:240 |
| 关键词 | Landslide susceptibility mapping Knowledge graph Knowledge representation learning Graph neural networks |
| ISSN号 | 1435-9529 |
| DOI | 10.1007/s10064-026-04886-3 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | Landslides are among the most widespread and destructive geological hazards in China, making landslide spatial predic-tion essential for refined disaster risk management. Although machine learning methods have achieved notable perfor-mance in landslide susceptibility assessment, they often inadequately represent mechanism knowledge, tend to focus on local-scale landslides and their surrounding environments, and strongly depend on training sample quality. Knowledge graphs provide a promising framework for systematically modeling landslide formation mechanisms and capturing latent relations among related entities. In this study, a landslide spatial prediction method, termed GeoSem-GraphSAGE, was proposed by integrating knowledge graphs and representation learning. A landslide knowledge graph was first constructed to explicitly model conditioning factors and their interaction relations, and semantic embeddings of graph nodes were learned using the ComplEx model. A directed weighted graph was established to capture geographical similarity beyond distance-based constraints, and the Louvain algorithm was applied for geographical partitioning to optimize non-landslide sample selection. Ultimately, the Graph sample and aggregate model was employed to jointly learn semantic informa-tion and graph structural features, enabling landslide susceptibility prediction. Experiments conducted in Anxi County demonstrated that the GeoSem-GraphSAGE outperformed representative non-graph-based models, achieving an accuracy of 90.22%, precision of 91.10%, recall of 89.18%, F1-score of 90.11%, and an AUC of 95.77%. Ablation results further indicated that explicit modeling of mechanism knowledge improved the delineation of susceptibility areas, while the geo-graphical similarity-based sampling strategy enhanced sample quality, leading to an overall performance improvement of approximately 2%-4%. This approach provides effective support for regional landslide risk mitigation. |
| URL标识 | 查看原文 |
| WOS关键词 | RESOLUTION |
| WOS研究方向 | Engineering ; Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001726816900004 |
| 出版者 | SPRINGER HEIDELBERG |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/221501]  |
| 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 | Li, Daichao |
| 作者单位 | 1.Fujian Geol Surveying & Mapping Inst, Fuzhou 350108, Peoples R China 2.Yangzhou Univ, Urban Planning & Dev Inst, Yangzhou 225127, Peoples R China; 3.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210000, Peoples R China; 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 5.Fuzhou Univ, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou 350108, Peoples R China; 6.Fuzhou Univ, Acad Digital China Fujian, 2 Xueyuan Rd, Fuzhou 350108, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Jin, Xinlei,Li, Daichao,Wang, Shu,et al. The research on landslide spatial prediction method based on knowledge graph and representation learning: a case study of Anxi county, Fujian Province[J]. BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT,2026,85(4):240. |
| APA | Jin, Xinlei.,Li, Daichao.,Wang, Shu.,Ye, Peng.,Gao, Jialiang.,...&Wu, Sheng.(2026).The research on landslide spatial prediction method based on knowledge graph and representation learning: a case study of Anxi county, Fujian Province.BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT,85(4),240. |
| MLA | Jin, Xinlei,et al."The research on landslide spatial prediction method based on knowledge graph and representation learning: a case study of Anxi county, Fujian Province".BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 85.4(2026):240. |
入库方式： OAI收割
来源：地理科学与资源研究所
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