A robust discretization method of factor screening for landslide susceptibility mapping using convolution neural network, random forest, and logistic regression models
文献类型：期刊论文
| 作者 | Zhao, Zheng3; Chen, Jianhua2 |
| 刊名 | INTERNATIONAL JOURNAL OF DIGITAL EARTH
 |
| 出版日期 | 2022-12-31 |
| 卷号 | 16期号:1页码:408-429 |
| ISSN号 | 1753-8955 |
| 关键词 | Discretization machine learning landslide susceptibility mapping spatial statistics convolution neural network |
| DOI | 10.1080/17538947.2023.2174192 |
| 文献子类 | Article |
| 英文摘要 | The selection of discretization criteria and interval numbers of landslide-related environmental factors generally fails to quantitatively determine or filter, resulting in uncertainties and limitations in the performance of machine learning (ML) methods for landslide susceptibility mapping (LSM). The aim of this study is to propose a robust discretization criterion (RDC) to quantify and explore the uncertainty and subjectivity of different discretization methods. The RDC consists of two steps: raw classification dataset generation and optimal dataset extraction. To evaluate the robustness of the proposed RDC method, Lushan County of Sichuan Province in China was chosen as the study area to generate the LSM based on three datasets (optimal dataset, original dataset with continuous values, and statistical dataset) using three popular ML methods, namely, convolution neural network, random forest, and logistic regression. The results show that the areas under the receiver operating characteristic curve (AUCs) of the optimal dataset for the abovementioned ML models are 0.963, 0.961, and 0.930 which are higher than those of the original dataset (0.938, 0.947, and 0.900) and statistical dataset (0.948, 0.954, and 0.897). In conclusion, the RDC method can extract the more representative features from environmental factors and outperform the other conventional discretization methods. |
| WOS关键词 | FREQUENCY RATIO ; SPATIAL PREDICTION ; EARTHQUAKE ; AREA ; BIVARIATE ; CHINA ; TREE ; MULTIVARIATE ; PERFORMANCE ; PROVINCE |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 出版者 | TAYLOR & FRANCIS LTD |
| WOS记录号 | WOS:000940225400001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/190228]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Chengdu Univ Technol, Coll Geophys, Chengdu 610059, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.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,Chen, Jianhua. A robust discretization method of factor screening for landslide susceptibility mapping using convolution neural network, random forest, and logistic regression models[J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2022,16(1):408-429. |
| APA | Zhao, Zheng,&Chen, Jianhua.(2022).A robust discretization method of factor screening for landslide susceptibility mapping using convolution neural network, random forest, and logistic regression models.INTERNATIONAL JOURNAL OF DIGITAL EARTH,16(1),408-429. |
| MLA | Zhao, Zheng,et al."A robust discretization method of factor screening for landslide susceptibility mapping using convolution neural network, random forest, and logistic regression models".INTERNATIONAL JOURNAL OF DIGITAL EARTH 16.1(2022):408-429. |
入库方式： OAI收割
来源：地理科学与资源研究所
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