Heterogeneous transfer learning considering feature representation and environmental consistency for landslide spatial prediction
文献类型：期刊论文
| 作者 | Zhao, Zheng4,5; Wang, Weihong3; Chen, Jianhua2; Yao, Jiaming1; Liao, Yangyang2; Liu, Jie4,5 |
| 刊名 | GISCIENCE & REMOTE SENSING
 |
| 出版日期 | 2024-12-31 |
| 卷号 | 61期号:1页码:2349343 |
| 关键词 | Landslide spatial prediction transfer learning environmental consistency feature representation random forest |
| DOI | 10.1080/15481603.2024.2349343 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | The difficult, time-consuming, and imbalanced acquisition of landslide inventories in complex and heterogeneous large areas often results in limited predictive performance for most statistical landslide spatial prediction methods. Although partial transfer learning methods have produced reliable predictive results and successfully implemented knowledge transfer between data-rich and data-scarce areas, most of these methods generally only extract inadequate environmental features and lack the ability to interpret when, what, and how to effectively transfer knowledge to other regions with limited data. In this paper, a heterogeneous transfer learning method considering feature representations and environmental consistency, which features robustness, similarity, and transferability, is proposed for landslide spatial prediction. Specifically, we trained a stacked autoencoder (SAE) to extract more nonlinear features among environmental factors, and added an environmental similarity criterion to the transfer adaptation boosting (TrAdaBoost) algorithm to minimize feature dissimilarities and avoid negative transfer in different scenarios. To evaluate the robustness of the proposed method, we first selected two target areas (Lushan County and Luding County, China) and a source area (Wenchuan County, China) as case study areas. Then, we directly combined the source area and target area as an additional dataset without considering transfer learning to validate the significance and necessity of the proposed method. The area under the receiver operating characteristic curve (AUC) of the two target regions for the proposed method were 0.920 and 0.972, respectively, which were greater than those of the traditional TrAdaBoost (0.909 and 0.969, respectively), SAE (0.790 and 0.937, respectively), and random forest (0.915 and 0.966, respectively) methods. Furthermore, the AUC values of the SAE (0.851 and 0.900) and random forest (0.890 and 0.935) models based on the expanded datasets were also lower than those of the proposed method. Therefore, the experimental results show that the proposed method can be generalized well due to its efficient utilization and high adaptability. Moreover, the proposed method can not only be applied to emergency rescue and disaster prevention but can also offer a promising way to improve landslide predictions of models with incomplete landslide inventories. |
| WOS关键词 | LOGISTIC-REGRESSION ; SUSCEPTIBILITY MODELS ; NEURAL-NETWORK ; RANDOM FOREST ; EARTHQUAKE ; INVENTORY ; ENSEMBLE ; RATIO |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| WOS记录号 | WOS:001217395100001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/205147]  |
| 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, Weihong |
| 作者单位 | 1.Peking Univ, Sch Earth & Space Sci, Beijing, Peoples R China 2.Chengdu Univ Technol, Coll Geophys, Chengdu, Peoples R China 3.Chengdu Univ Technol, Coll Earth & Planetary Sci, Chengdu, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Zheng,Wang, Weihong,Chen, Jianhua,et al. Heterogeneous transfer learning considering feature representation and environmental consistency for landslide spatial prediction[J]. GISCIENCE & REMOTE SENSING,2024,61(1):2349343. |
| APA | Zhao, Zheng,Wang, Weihong,Chen, Jianhua,Yao, Jiaming,Liao, Yangyang,&Liu, Jie.(2024).Heterogeneous transfer learning considering feature representation and environmental consistency for landslide spatial prediction.GISCIENCE & REMOTE SENSING,61(1),2349343. |
| MLA | Zhao, Zheng,et al."Heterogeneous transfer learning considering feature representation and environmental consistency for landslide spatial prediction".GISCIENCE & REMOTE SENSING 61.1(2024):2349343. |
入库方式： OAI收割
来源：地理科学与资源研究所
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