A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods

doi:10.1016/j.catena.2019.104188

	A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods
	Zhu, A-Xing 1,2,3,4,5,6,7; Miao, Yamin 2,4,6; Liu, Junzhi 2,4,6; Bai, Shibiao 2,4,6; Zeng, Canying 8; Ma, Tianwu 2,4,6; Hong, Haoyuan 2,4,6
刊名	CATENA
	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.
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关键词	SUPPORT VECTOR MACHINE ; EARTHQUAKE-TRIGGERED LANDSLIDES ; LOGISTIC-REGRESSION ; WENCHUAN EARTHQUAKE ; SPATIAL PREDICTION ; FREQUENCY RATIO ; NEURAL-NETWORKS ; GIS ; MODELS ; STRATEGIES
WOS研究方向	Geology ; Agriculture ; Water Resources
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000488417700014
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
内容类型	期刊论文
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.Southern Univ Sci & Technol, Ctr Social Sci, Guangzhou, Guangdong, Peoples R China 2.Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, 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.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China 5.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA 6.Nanjing Normal Univ, Sch Geog, Nanjing 210023, Jiangsu, Peoples R China 7.Univ Chinese Acad Sci, Beijing 100049, 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.