IGSNRR OpenIR
A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping
Zhu, A-Xing1,2,3,4; Miao, Yamin1,2,3; Wang, Rongxun5; Zhu, Tongxin5; Deng, Yongcui1; Liu, Junzhi1,2,3; Yang, Lin6; Qin, Cheng-Zhi6; Hong, Haoyuan1,2,3
2018-07-01
Source PublicationCATENA
ISSN0341-8162
Volume166Pages:317-327
Corresponding AuthorLiu, Junzhi(liujunzhi@njnu.edu.cn) ; Hong, Haoyuan(hong_haoyuan@outlook.com)
AbstractIn 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.
KeywordData-driven models Expert knowledge-based model Landslide susceptibility GIS Logistic regression Artificial neural network
DOI10.1016/j.catena.2018.04.003
WOS KeywordARTIFICIAL NEURAL-NETWORKS ; 3 GORGES AREA ; SPATIAL PREDICTION MODELS ; LOGISTIC-REGRESSION ; FUZZY-LOGIC ; WENCHUAN EARTHQUAKE ; SAMPLING STRATEGIES ; GIS TECHNOLOGY ; YANGTZE-RIVER ; HAZARD
Indexed BySCI
Language英语
Funding ProjectNational 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
Funding OrganizationNational 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
WOS Research AreaGeology ; Agriculture ; Water Resources
WOS SubjectGeosciences, Multidisciplinary ; Soil Science ; Water Resources
WOS IDWOS:000434238800031
PublisherELSEVIER SCIENCE BV
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54869
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLiu, Junzhi; Hong, Haoyuan
Affiliation1.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
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhu, A-Xing]'s Articles
[Miao, Yamin]'s Articles
[Wang, Rongxun]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhu, A-Xing]'s Articles
[Miao, Yamin]'s Articles
[Wang, Rongxun]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhu, A-Xing]'s Articles
[Miao, Yamin]'s Articles
[Wang, Rongxun]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.