Natural Hazards

, Volume 97, Issue 2, pp 579–609 | Cite as

Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the Dongjiang Reservoir Watershed, China

  • Jie DouEmail author
  • Ali P. Yunus
  • Yueren Xu
  • Zhongfan Zhu
  • Chi-Wen Chen
  • Mehebub Sahana
  • Khabat Khosravi
  • Yong Yang
  • Binh Thai PhamEmail author
Original Paper


This study investigated the characteristics of rainfall-triggered landslides during the Typhoon Bilis in the Dongjiang Reservoir Watershed, China. The comparative shallow landslide susceptibility mappings (LSMs) were produced by the ensemble data-driven statistical models in a GIS environment. At first, the landslide inventory for the study area was prepared from the high-resolution QuickBird images, and China–Brazil Earth Resources Satellite images, and field survey. Other necessary data for landslide susceptibility analysis such as the amount of rainfall, geology, and topography were also collected from the respective agencies. Twelve predisposing factors were then prepared using this available dataset. To reduce the subjectivity of models and caution in the selection of predisposing factors, and to avoid the spatial autocorrelation redundancy, certainty factor approach was attempted to optimize these twelve set of parameters. For validating the accuracy of the model, the original landslide data were randomly divided into two parts: 70% (1545 landslides) for training the model and the remaining 30% (662 landslides) for validation. The verified results showed that using the optimized predisposing factors has a higher performance than using all the original twelve factors. The results of ensemble models also showed that LSM maps prepared using binary logistic regression (accuracy is 0.848) model are more accurate than those prepared using bivariate statistical analysis (accuracy is 0.837) model. Additionally, our analysis concludes that the short duration and high-intensity rainfall, drainage density, lithology, and curvature are the major influencing factors for landslide occurrences in this case study area. This research provides an improved understanding of the mechanism of landslides caused by the typhoons for the adjoining watersheds nearby the reservoir. The preliminary understandings and approach could also be applied in similar geological and rainfall-triggered case study sites in the other parts of the world for risk mitigation.


Shallow landslide Certainty factor Binary logistic regression Torrential rainfall Typhoon Bilis 



The authors would like to thank Professor Dr. Li Tiefeng of China Geological Survey (CGS) for providing the satellite image data. Dou also expresses his great gratitude to Dr. Uttam, and Dr. Zou Yi for their constructive comments and support. This research work has been supported by the National Key R&D Program of China (ID: 2018YFC1504803) and the National Nature Science Foundation of China (Grant Nos. 51679127 and 51439003).


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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Key Laboratory of Geological Hazards on Three Gorges Reservoir Area (China Three Gorges University)Ministry of EducationYichangChina
  2. 2.Department of Civil and Environmental EngineeringNagaoka University of TechnologyNagaokaJapan
  3. 3.Department of Remote Sensing and GIS ApplicationsAligarh Muslim UniversityAligarhIndia
  4. 4.State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (SKLGP)Chengdu University of TechnologyChengduChina
  5. 5.Key Laboratory of Earthquake PredicationInstitute of Earthquake Forecasting, China Earthquake AdministrationBeijingChina
  6. 6.College of Water SciencesBeijing Normal UniversityBeijingChina
  7. 7.National Science and Technology Center for Disaster ReductionNew Taipei CityTaiwan
  8. 8.Department of GeographyJamia Millia IslamiaNew DelhiIndia
  9. 9.Institute of Industrial ScienceThe University of TokyoTokyoJapan
  10. 10.Department of Watershed ManagementSari Agricultural Science and Natural Resources UniversitySariIran
  11. 11.Institute of Research and DevelopmentDuy Tan UniversityDa NangVietnam

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