Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods

  • Haoyuan Hong
  • Himan Shahabi
  • Ataollah Shirzadi
  • Wei Chen
  • Kamran Chapi
  • Baharin Bin Ahmad
  • Majid Shadman Roodposhti
  • Arastoo Yari Hesar
  • Yingying Tian
  • Dieu Tien BuiEmail author
Original Paper


The aim of this research is to investigate multi-criteria decision making [spatial multi-criteria evaluation (SMCE)], bivariate statistical methods [frequency ratio (FR), index of entropy (IOE), weighted linear combination (WLC)] and machine learning [support vector machine (SVM)] models for estimating landslide susceptibility at the Wuning area, China. A total of 445 landslides were randomly classified into 70% (311 landslides) and 30% (134 landslides) to train and validate landslide models, respectively. Fourteen landslide conditioning factors including slope angle, slope aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, NDVI, land use, rainfall, distance to road, distance to river and distance to fault were then studied for landslide susceptibility assessment. Performances of five studied models were evaluated using area under the ROC curve (AUROC) for training (success rate curve) and validation (prediction rate curve) datasets, statistical-based measures and tests. Results indicated that the area under the success rate curve for the FR, IOE, WLC, SVM and SMCE models was 88.32%, 82.58%, 78.91%, 85.47% and 89.96%, respectively, demonstrating that SMCE could provide the higher accuracy. The prediction capability findings revealed that the SMCE model (AUC = 86.81%) was also the highest approach among the five studied models, followed by the FR (AUC = 84.53%), the SVM (AUC = 81.24%), the IOE (AUC = 79.67%) and WLC (73.92%) methods. The landslide susceptibility maps derived from the above five models are reasonably accurate and could be used to perform elementary land use planning for hazard extenuation.


Landslide susceptibility Natural disaster Support vector machine Spatial multi-criteria evaluation Weighted linear combination 



The authors would like to thank editor and anonymous reviewers for their meaningful comments on the primary version of the manuscript. This research was supported by the Universiti Teknologi Malaysia (UTM) based on Research University Grant (Q.J130000.2527.17H84).


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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Haoyuan Hong
    • 1
    • 2
    • 3
  • Himan Shahabi
    • 4
  • Ataollah Shirzadi
    • 5
  • Wei Chen
    • 6
  • Kamran Chapi
    • 5
  • Baharin Bin Ahmad
    • 7
  • Majid Shadman Roodposhti
    • 8
  • Arastoo Yari Hesar
    • 9
  • Yingying Tian
    • 10
  • Dieu Tien Bui
    • 11
    • 12
    Email author
  1. 1.Key Laboratory of Virtual Geographic EnvironmentNanjing Normal UniversityNanjingPeople’s Republic of China
  2. 2.State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province)NanjingPeople’s Republic of China
  3. 3.Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and ApplicationNanjingPeople’s Republic of China
  4. 4.Department of Geomorphology, Faculty of Natural ResourcesUniversity of KurdistanSanandajIran
  5. 5.Department of Rangeland and Watershed Management, Faculty of Natural ResourcesUniversity of KurdistanSanandajIran
  6. 6.College of Geology and EnvironmentXi’an University of Science and TechnologyXi’anPeople’s Republic of China
  7. 7.Department of Geoinformation, Faculty of Geoinformation and Real EstateUniversiti Teknologi Malaysia (UTM)Johor BahruMalaysia
  8. 8.Discipline of Geography and Spatial Sciences, School of Land and FoodUniversity of TasmaniaHobartAustralia
  9. 9.Department of GeographyUniversity of Mohaghegh ArdabiliArdabilIran
  10. 10.Jiangxi Provincial Meteorological Observatory, Jiangxi Meteorological BureauNanchangPeople’s Republic of China
  11. 11.Geographic Information Science Research GroupTon Duc Thang UniversityHo Chi Minh CityVietnam
  12. 12.Faculty of Environment and Labour SafetyTon Duc Thang UniversityHo Chi Minh CityVietnam

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