GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China
文献类型：期刊论文
| 作者 | Zhao Yu1,2; Wang Rui1 ; Jiang Yuanjun3; Liu Huajun4; Wei Zhenlei1
|
| 刊名 | Engineering Geology
 |
| 出版日期 | 2019 |
| 卷号 | 259页码:UNSP 105147 |
| 关键词 | Curves (road) Geographic information systems Rain Regression analysis Remote sensing |
| ISSN号 | 0013-7952 |
| DOI | 10.1016/j.enggeo.2019.105147 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | The logistic regression (LR) method was applied to assess landslide susceptibility in the northern Yueqing of Zhejiang Province, China. An inventory map of 323 landslides, digital elevation models (DEMs), remote sensing images, geological map, roads and rivers were collected and utilized in the analysis. LR models using partially continuous (LR-CON) and all categorized variables (LR-CAT) were carried out under different grid sizes of 5 m, 15 m and 30 m to investigate the influence of variable type and grid size on landslide susceptibility assessment. Ten different subsets of positive (landslide) and negative (non-landslide) cases were prepared for each kind of LR models. Receiver operation characteristic (ROC) curves were employed to evaluate the performance of the LR models while cross-validation was used to validate the effectiveness of susceptibility maps. The models with and without the rainfall factor were also compared. Among the three grid sizes, the result of 15 m shows the best performance with mean AUC (the area under a ROC curve) of 82.6%. The AUC values of LR-CON models with different grid sizes all demonstrated acceptable fit (0.7 0.8), indicating that LR method has a better performance when using all categorical variables than using partially continuous variables. Random sampling is an adoptable method to generate training group and there is no significant difference of AUC values among different data subsets. The results also showed that the accuracy of the landslide susceptibility models is higher when rainfall is included in the analyses. © 2019 |
| 语种 | 英语 |
| WOS记录号 | WOS:000487567400008 |
| 源URL | [http://ir.imde.ac.cn/handle/131551/26625]  |
| 专题 | 成都山地灾害与环境研究所_山地灾害与地表过程重点实验室 |
| 通讯作者 | Jiang Yuanjun |
| 作者单位 | 1.College of Civil Engineering and Architecture, Zhejiang University, Hangzhou;310058, China; 2.MOE Key Laboratory of Soft Soil and Geoenvironmental Engineering, Zhejiang University, Hangzhou;310058, China; 3.Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, China; 4.Hydrogeology Section, Zhejiang Bureau of Geology and Mineral Resource Exploration and Development, Ningbo;315000, China |
| 推荐引用方式 GB/T 7714 | Zhao Yu,Wang Rui,Jiang Yuanjun,et al. GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China[J]. Engineering Geology,2019,259:UNSP 105147. |
| APA | Zhao Yu,Wang Rui,Jiang Yuanjun,Liu Huajun,&Wei Zhenlei.(2019).GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China.Engineering Geology,259,UNSP 105147. |
| MLA | Zhao Yu,et al."GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China".Engineering Geology 259(2019):UNSP 105147. |
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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