Bivariate Landslide Susceptibility Analysis: Clarification, Optimization, Open Software, and Preliminary Comparison
文献类型：期刊论文
| 作者 | Li, Langping; Lan, Hengxing2,3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-03-01 |
| 卷号 | 15期号:5 |
| 关键词 | landslide susceptibility bivariate clarification optimization software comparison |
| ISSN号 | 2072-4292 |
| DOI | 10.3390/rs15051418 |
| 文献子类 | Article |
| 英文摘要 | Bivariate data-driven methods have been widely used in landslide susceptibility analysis. However, the names, principles, and correlations of bivariate methods are still confused. In this paper, the names, principles, and correlations of bivariate methods are first clarified based on a comprehensive and in-depth survey. A total of eleven prevalent bivariate methods are identified, nominated, and elaborated in a general framework, constituting a well-structured bivariate method family. We show that all prevalent bivariate methods depend on empirical conditional probabilities of landslide occurrence to calculate landslide susceptibilities, either exclusively or inclusively. It is clarified that those eight conditional-probability-based bivariate methods, which exclusively depend on empirical conditional probabilities, are particularly strongly correlated in principle, and therefore are expected to have a very close or even the same performance. It is also suggested that conditional-probability-based bivariate methods apply to a classification-free modification, in which factor classifications are avoided and the result is dominated by a single parameter, bin width. Then, a general optimization framework for conditional-probability-based bivariate methods, based on the classification-free modification and obtaining optimum results by optimizing the dominant parameter bin width, is proposed. The open software Automatic Landslide Susceptibility Analysis (ALSA) is updated to implement the eight conditional-probability-based bivariate methods and the general optimization framework. Finally, a case study is presented, which confirms the theoretical expectation that different conditional-probability-based bivariate methods have a very close or even the same performance, and shows that optimal bivariate methods perform better than conventional bivariate methods regarding both the prediction rate and the ability to reveal the quasi-continuous varying pattern of sensibilities to landslides for individual predisposing factors. The principles and open software presented in this study provide both theoretical and practical foundations for applications and explorations of bivariate methods in landslide susceptibility analysis. |
| WOS关键词 | WEIGHTS-OF-EVIDENCE ; 2013 LUSHAN EARTHQUAKE ; LIKELIHOOD RATIO ; SPATIAL-DISTRIBUTION ; LOGISTIC-REGRESSION ; SICHUAN PROVINCE ; XINMO LANDSLIDE ; FREQUENCY RATIO ; FUZZY ; PREDICTION |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:000948028500001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/190264]  |
| 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.Changan Univ, Minist Nat Resources, Key Lab Ecol Geol & Disaster Prevent, Xian 710064, Peoples R China 2.Changan Univ, Sch Geol Engn & Geomat, Xian 710064, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Langping,Lan, Hengxing. Bivariate Landslide Susceptibility Analysis: Clarification, Optimization, Open Software, and Preliminary Comparison[J]. REMOTE SENSING,2023,15(5). |
| APA | Li, Langping,&Lan, Hengxing.(2023).Bivariate Landslide Susceptibility Analysis: Clarification, Optimization, Open Software, and Preliminary Comparison.REMOTE SENSING,15(5). |
| MLA | Li, Langping,et al."Bivariate Landslide Susceptibility Analysis: Clarification, Optimization, Open Software, and Preliminary Comparison".REMOTE SENSING 15.5(2023). |
入库方式： OAI收割
来源：地理科学与资源研究所
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