GIS-based evaluation of landslide susceptibility using a novel hybrid computational intelligence model on different mapping units
文献类型：期刊论文
| 作者 | Zhang Ting-yu; Mao Zhong-an; Wang Tao |
| 刊名 | JOURNAL OF MOUNTAIN SCIENCE
 |
| 出版日期 | 2020 |
| 卷号 | 17期号:12页码:2929-2941 |
| 关键词 | Kernel logistic regression model Landslide susceptibility GIS Fractal dimension |
| ISSN号 | 1672-6316 |
| DOI | 10.1007/s11629-020-6393-8 |
| 英文摘要 | Landslide susceptibility mapping is significant for landslide prevention. Many approaches have been used for landslide susceptibility prediction, however, their performances are unstable. This study constructed a hybrid model, namely box counting dimension-based kernel logistic regression model, which uses fractal dimension calculated by box counting method as input data based on grid cells mapping unit and terrain mapping unit. The performance of this model was evaluated in the application in Zhidan County, Shaanxi Province, China. Firstly, a total of 221 landslides were identified and mapped, and 11 landslide predisposing factors were considered. Secondly, the landslide susceptibility maps (LSMs) of the study area were obtained by constructing the model on two different mapping units. Finally, the results were evaluated with five statistical indexes, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and Accuracy. The statistical indexes of the model obtained on the terrain mapping unit were larger than those based on grid cells mapping unit. For training and validation datasets, the area under the receiver operating characteristic curve (AUC) of the model based on terrain mapping unit were 0.9374 and 0.9527, respectively, indicating that establishing this model on the terrain mapping unit was advantageous in the study area. The results show that the fractal dimension improves the prediction ability of the kernel logistic model. In addition, the terrain mapping unit is a more promising mapping unit in Loess areas. |
| URL标识 | 查看原文 |
| 语种 | 英语 |
| 源URL | [http://ir.imde.ac.cn/handle/131551/57071]  |
| 专题 | Journal of Mountain Science_Journal of Mountain Science-2020_Vol17 No.12 |
| 推荐引用方式 GB/T 7714 | Zhang Ting-yu,Mao Zhong-an,Wang Tao. GIS-based evaluation of landslide susceptibility using a novel hybrid computational intelligence model on different mapping units[J]. JOURNAL OF MOUNTAIN SCIENCE,2020,17(12):2929-2941. |
| APA | Zhang Ting-yu,Mao Zhong-an,&Wang Tao.(2020).GIS-based evaluation of landslide susceptibility using a novel hybrid computational intelligence model on different mapping units.JOURNAL OF MOUNTAIN SCIENCE,17(12),2929-2941. |
| MLA | Zhang Ting-yu,et al."GIS-based evaluation of landslide susceptibility using a novel hybrid computational intelligence model on different mapping units".JOURNAL OF MOUNTAIN SCIENCE 17.12(2020):2929-2941. |
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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