Natural Hazards

, Volume 78, Issue 3, pp 1749–1776 | Cite as

An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan

  • Jie Dou
  • Hiromitsu Yamagishi
  • Hamid Reza Pourghasemi
  • Ali P. Yunus
  • Xuan Song
  • Yueren Xu
  • Zhongfan Zhu
Original Paper


The objective of this study was to select the maximum number of correlated factors with landslide occurrence for slope-instability mapping and assess landslide susceptibility on Osado Island, Niigata Prefecture, Central Japan, integrating two techniques, namely certainty factor (CF) and artificial neural network (ANN), in a geographic information system (GIS) environment. The landslide inventory data of the National Research Institute for Earth Science and Disaster Prevention (NIED) and a 10-m digital elevation model (DEM) from the Geographical Survey of Institute, Japan, were analyzed. Our study identified fourteen possible landslide-conditioning factors. Considering the spatial autocorrelation and factor redundancy, we applied the CF approach to optimize these set of variables. We hypothesize that if the thematic factor layers of the CF values are positive, it implies that these conditioning factors have a correlation with the landslide occurrence. Therefore, based on this assumption and because of their positive CF values, six conditioning factors including slope angle (0.04), slope aspect (0.02), drainage density network (0.34), distance to the geologic boundaries (0.37), distance to fault (0.35), and lithology (0.31) have been selected as landslide-conditioning factors for further analysis. We partitioned the data into two groups: 70 % (520 landslide locations) for model training and the remaining 30 % (220 landslide locations) for validation. Then, a common ANN model, namely the back-propagation neural network (BPNN), was employed to produce the landslide susceptibility maps. The receiver operating characteristics including the area under the curve (AUC) were used to assess the model accuracy. The validation results indicate that the values of the AUC at optimized and non-optimized BPNN were 0.82 and 0.73, respectively. Hence, it is concluded that the optimized factor model can provide superior accuracy in the prediction of landslide susceptibility in the study area. In this context, we propose a method to select the factors with landslide occurrence. This work is fundamental for further study of the landslide susceptibility evaluation and prediction.


Landslide susceptibility Certainty factor BPNN AUC Osado Island 



We would like to express our gratitude to Midori NET Niigata and Sado City for providing the ortho photographs of Sado Island and the NIED for providing the landslide data.


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Jie Dou
    • 1
  • Hiromitsu Yamagishi
    • 2
  • Hamid Reza Pourghasemi
    • 3
  • Ali P. Yunus
    • 1
  • Xuan Song
    • 4
  • Yueren Xu
    • 5
  • Zhongfan Zhu
    • 6
  1. 1.Department of Natural Environmental StudiesThe University of TokyoKashiwaJapan
  2. 2.Asian Institute of Space InformationSapporoJapan
  3. 3.Department of Natural Resources and Environment, College of AgricultureShiraz UniversityShirazIran
  4. 4.Center for Spatial Information ScienceThe University of TokyoKashiwaJapan
  5. 5.Institute of Earthquake ScienceChina Earthquake AdministrationBeijingChina
  6. 6.College of Water SciencesBeijing Normal UniversityBeijingChina

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