A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods
文献类型：期刊论文
| 作者 | Zhu, A-Xing1,2,3,4,5,6,7; Miao, Yamin1,2,4; Liu, Junzhi1,2,4; Bai, Shibiao1,2,4; Zeng, Canying8; Ma, Tianwu1,2,4; Hong, Haoyuan1,2,4 |
| 刊名 | CATENA
 |
| 出版日期 | 2019-12-01 |
| 卷号 | 183页码:17 |
| 关键词 | Landslide absence data Sampling method Data-driven methods Landslide susceptibility mapping, similarity model, data mining and machine learning |
| ISSN号 | 0341-8162 |
| DOI | 10.1016/j.catena.2019.104188 |
| 通讯作者 | Liu, Junzhi(liujunzhi@njnu.edu.cn) ; Hong, Haoyuan(171301013@stu.njnu.edu.cn) |
| 英文摘要 | The absence data (samples) for landslide susceptibility mapping using data-driven methods are not available directly and often approximated by locations where no landslides have occurred. The existing methods for generating absence data cannot quantify the reliability of candidate absence data and thus such data reduce the quality of prediction. In this paper, a new approach to absence data generation, referred to as similarity based sampling, was proposed for landslide susceptibility mapping using data-driven methods. First, the reliability of candidate absence data is quantified based on the dissimilarity in environmental conditions (covariate conditions) between the absence data and the presence data (which are the landslide occurrences). The absence data whose reliability value is higher than a given threshold were selected to be used. The proposed approach was validated through its application to three data-driven methods (i.e. logistic regression, support vector machine and random forest) for landslide susceptibility mapping. A case study was conducted in the Youfang catchment in southern Gansu Province of China. Ten groups of absence data were generated each corresponding to one of the ten different thresholds of reliability ranging from 0.0 to 0.9. The results show that the prediction accuracy of the data-driven methods rose when the threshold increased from 0.0 to 0.5, but the accuracy decreases as the threshold continues to increase after 0.5, that is, from 0.5 to 0.9. The best performance was obtained when the threshold was 0.5. The proposed method was compared with existing methods for absence data generation (i.e. buffer controlled and target space exteriorization). These results show that the similarity-based approach has a better performance than these existing methods for landslide susceptibility mapping using data-driven methods. |
| WOS关键词 | SUPPORT VECTOR MACHINE ; EARTHQUAKE-TRIGGERED LANDSLIDES ; LOGISTIC-REGRESSION ; WENCHUAN EARTHQUAKE ; SPATIAL PREDICTION ; FREQUENCY RATIO ; NEURAL-NETWORKS ; GIS ; MODELS ; STRATEGIES |
| 资助项目 | National Natural Science Foundation of China[41431177] ; National Natural Science Foundation of China[41871300] ; National Basic Research Program of China[2015CB954102] ; PAPD ; Outstanding Innovation Team in Colleges and Universities in Jiangsu Province ; University of Wisconsin-Madison |
| WOS研究方向 | Geology ; Agriculture ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000488417700014 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China ; National Basic Research Program of China ; PAPD ; Outstanding Innovation Team in Colleges and Universities in Jiangsu Province ; University of Wisconsin-Madison |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/129596]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Liu, Junzhi; Hong, Haoyuan |
| 作者单位 | 1.Nanjing Normal Univ, Sch Geog, Nanjing 210023, Jiangsu, Peoples R China 2.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, 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 4.Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing 210023, Jiangsu, Peoples R China 5.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA 6.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 7.Southern Univ Sci & Technol, Ctr Social Sci, Guangzhou, Guangdong, Peoples R China 8.Zhejiang Univ Finance & Econ, Inst Land & Urban Rural Dev, Hangzhou 310018, Zhejiang, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, A-Xing,Miao, Yamin,Liu, Junzhi,et al. A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods[J]. CATENA,2019,183:17. |
| APA | Zhu, A-Xing.,Miao, Yamin.,Liu, Junzhi.,Bai, Shibiao.,Zeng, Canying.,...&Hong, Haoyuan.(2019).A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods.CATENA,183,17. |
| MLA | Zhu, A-Xing,et al."A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods".CATENA 183(2019):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
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