Landslide Susceptibility Assessment Based on TFPF-SU and AuFNN Methods: A Case Study of Dongchuan District, Yunnan Province
文献类型：期刊论文
| 作者 | Li, Kuan1; Sun, Yuqiang2; Fan, Junfu1,3; Li, Ping1 |
| 刊名 | APPLIED SCIENCES-BASEL
 |
| 出版日期 | 2026-01-20 |
| 卷号 | 16期号:2页码:1035 |
| 关键词 | slope unit landslide evaluation factors susceptibility analysis machine learning |
| DOI | 10.3390/app16021035 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | Landslides are a common type of geological hazard, characterized by sudden onset, high destructiveness, and frequent occurrence, and are widely distributed in mountainous areas with complex terrain. In recent years, due to extreme weather and intensified human activities, both the frequency and intensity of landslide disasters in China have increased significantly, posing serious threats to human life, property, and socio-economic development. Although various methods for landslide susceptibility assessment have been proposed, the accuracy of existing models still needs improvement. In this context, this study takes the landslide-prone Dongchuan District of Kunming City, Yunnan Province, as a case study and proposes a coupled model that integrates an autoencoder and a feedforward neural network (AuFNN). The model uses the autoencoder to extract low-dimensional and highly discriminative feature representations, which are then used as input to the feedforward neural network to perform landslide susceptibility assessment. To evaluate the effectiveness of the proposed model, it is compared with four commonly used models, Support Vector Machine (SVM), Random Forest (RF), XGBoost, and Feedforward Neural Network (FNN), based on performance metrics such as the ROC curve, recall, and F1 score. The results indicate that the AuFNN model provides an alternative representation learning framework and achieves performance comparable to that of established machine learning models in landslide susceptibility assessment, as reflected by similar AUC, accuracy, and F1 score values. |
| URL标识 | 查看原文 |
| WOS关键词 | GIS |
| WOS研究方向 | Chemistry ; Engineering ; Materials Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001670094900001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/221033]  |
| 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 | Fan, Junfu |
| 作者单位 | 1.Shandong Univ Technol, Sch Civil Engn & Geomat, Zibo 255000, Peoples R China; 2.CCCC Highway Consultants Co Ltd, Beijing 100088, Peoples R China; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Kuan,Sun, Yuqiang,Fan, Junfu,et al. Landslide Susceptibility Assessment Based on TFPF-SU and AuFNN Methods: A Case Study of Dongchuan District, Yunnan Province[J]. APPLIED SCIENCES-BASEL,2026,16(2):1035. |
| APA | Li, Kuan,Sun, Yuqiang,Fan, Junfu,&Li, Ping.(2026).Landslide Susceptibility Assessment Based on TFPF-SU and AuFNN Methods: A Case Study of Dongchuan District, Yunnan Province.APPLIED SCIENCES-BASEL,16(2),1035. |
| MLA | Li, Kuan,et al."Landslide Susceptibility Assessment Based on TFPF-SU and AuFNN Methods: A Case Study of Dongchuan District, Yunnan Province".APPLIED SCIENCES-BASEL 16.2(2026):1035. |
入库方式： OAI收割
来源：地理科学与资源研究所
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