Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models
文献类型：期刊论文
| 作者 | Chen Tao; Zhu Li; Niu Rui-qing; Trinder, C. John; Peng Ling; Lei Tao |
| 刊名 | JOURNAL OF MOUNTAIN SCIENCE
 |
| 出版日期 | 2020 |
| 卷号 | 17期号:3页码:670-685 |
| 关键词 | Mapping landslide susceptibility Gradient boosting decision tree Random forest Information value model Three Gorges Reservoir |
| ISSN号 | 1672-6316 |
| DOI | 10.1007/s11629-019-5839-3 |
| 文献子类 | Article |
| 英文摘要 | This work was to generate landslide susceptibility maps for the Three Gorges Reservoir (TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree (GBDT), random forest (RF) and information value (InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area, 28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic (ROC) curves, the sensitivity, specificity, overall accuracy (OA), and kappa coefficient (KAPPA). The results showed that the GBDT, RF and InV models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR. |
| 电子版国际标准刊号 | 1993-0321 |
| 语种 | 英语 |
| WOS记录号 | WOS:000519163500013 |
| 源URL | [http://ir.imde.ac.cn/handle/131551/46785]  |
| 专题 | Journal of Mountain Science_Journal of Mountain Science-2020_Vol17 No.3 |
| 推荐引用方式 GB/T 7714 | Chen Tao,Zhu Li,Niu Rui-qing,et al. Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models[J]. JOURNAL OF MOUNTAIN SCIENCE,2020,17(3):670-685. |
| APA | Chen Tao,Zhu Li,Niu Rui-qing,Trinder, C. John,Peng Ling,&Lei Tao.(2020).Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models.JOURNAL OF MOUNTAIN SCIENCE,17(3),670-685. |
| MLA | Chen Tao,et al."Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models".JOURNAL OF MOUNTAIN SCIENCE 17.3(2020):670-685. |
入库方式： OAI收割
来源：成都山地灾害与环境研究所
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