A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping

doi:10.1016/j.catena.2018.04.003

	A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping
	Zhu, A-Xing 1,2,3,4; Miao, Yamin 1,2,3; Wang, Rongxun 5; Zhu, Tongxin 5; Deng, Yongcui 1; Liu, Junzhi 1,2,3; Yang, Lin 6; Qin, Cheng-Zhi 6; Hong, Haoyuan 1,2,3
刊名	CATENA
	2018-07-01
卷号	166 页码:317-327
关键词	Data-driven models Expert knowledge-based model Landslide susceptibility GIS Logistic regression Artificial neural network
ISSN号	0341-8162
DOI	10.1016/j.catena.2018.04.003
通讯作者	Liu, Junzhi(liujunzhi@njnu.edu.cn) ; Hong, Haoyuan(hong_haoyuan@outlook.com)
英文摘要	In this study, an expert knowledge-based model, a logistic regression model, and an artificial neural network model were compared for their accuracy and portability in landslide susceptibility mapping. Two study areas (the Kaixian and the Three Gorges areas in China) were selected for this comparison based on their well-known, high landslide hazard. To evaluate the performance of these models and to minimize the impact of the particularity of a study area on model performance, cross-applications of three models between the two study areas were conducted. When the Kaixian area was used as a model development area, prediction accuracy for the expert knowledge-based model, the logistic regression model, and the artificial neural network model were 71.5%, 81.0% and 100.0%, respectively. The high prediction accuracy of the two data-driven models were expected because the observed landslide occurrence used in training the models were also used to validate their respective performance, while the expert knowledge-based model did not use these observations for training. The perfect accuracy for the neural network model can also be attributed to its over-prediction of the susceptibility. When breaking the susceptibility into four classes: very low susceptibility (0-0.25), low susceptibility (0.25-0.5), high susceptibility (0.5-0.75), and very high susceptibility (0.75-1), the observed landslide density at the very high susceptibility level is 0.303/km(2), 0.212/km(2), and 0.195/km(2) for the expert knowledge-based model, the logistic regression model, and the artificial neural network model, respectively. This suggests that the expert knowledge-based model was much better than the other two data-driven models at evaluating landslide occurrence in very high susceptibility areas. When the three models developed in the Kaixian area were applied in the Three Gorges area without any changes, their prediction accuracy dropped to 44.8% for the logistic regression model and 81.6% for the artificial neural network model, while the expert knowledge-based model maintained its initial accuracy level of 82.8%. The landslide density for the very high susceptibility areas in the Three Georges area was 0.275/km(2), 0.082/km(2), and 0.060/Km(2) for the expert knowledge-based model, the logistic model, and the artificial neural network model, respectively. These results indicate that the expert knowledge-based model is more effective at predicting areas with very high susceptibility. When the Three Gorges area was used as a model development area and the Kaixian area was used as the model application area, similar results were obtained Results from the two experiments show that the performance of the logistic regression model and artificial neural network model is not as stable as the expert knowledge-based model when transferred to a new area. This suggests that the expert knowledge-based model is more suitable for landslide susceptibility mapping over large areas.
资助项目	National Natural Science Foundation of China[41431177] ; National Natural Science Foundation of China[41601413] ; Natural Science Research Program of Jiangsu[BK20150975] ; Natural Science Foundation of the Jiangsu Higher Education Institutions of China[14KJA170001] ; National Basic Research Program of China[2015CB954102] ; PAPD program of Jiangsu Higher Education Institutions[164320H116] ; University of Wisconsin-Madison ; One-Thousand Talents Program of China
WOS关键词	ARTIFICIAL NEURAL-NETWORKS ; 3 GORGES AREA ; SPATIAL PREDICTION MODELS ; LOGISTIC-REGRESSION ; FUZZY-LOGIC ; WENCHUAN EARTHQUAKE ; SAMPLING STRATEGIES ; GIS TECHNOLOGY ; YANGTZE-RIVER ; HAZARD
WOS研究方向	Geology ; Agriculture ; Water Resources
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000434238800031
资助机构	National Natural Science Foundation of China ; Natural Science Research Program of Jiangsu ; Natural Science Foundation of the Jiangsu Higher Education Institutions of China ; National Basic Research Program of China ; PAPD program of Jiangsu Higher Education Institutions ; University of Wisconsin-Madison ; One-Thousand Talents Program of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/54868]
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, Key Lab Virtual Geog Environm, Nanjing 210023, Jiangsu, Peoples R China 2.State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Jiangsu, Peoples R China 3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China 4.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA 5.Univ Minnesota, Dept Geog, Duluth, MN 55812 USA 6.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	Zhu, A-Xing,Miao, Yamin,Wang, Rongxun,et al. A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping[J]. CATENA,2018,166:317-327.
APA	Zhu, A-Xing.,Miao, Yamin.,Wang, Rongxun.,Zhu, Tongxin.,Deng, Yongcui.,...&Hong, Haoyuan.(2018).A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping.CATENA,166,317-327.
MLA	Zhu, A-Xing,et al."A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping".CATENA 166(2018):317-327.