Environmental Earth Sciences

, Volume 74, Issue 1, pp 413–429 | Cite as

Forecasting and validation of landslide susceptibility using an integration of frequency ratio and neuro-fuzzy models: a case study of Seorak mountain area in Korea

Original Article
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Abstract

Landslides susceptibility maps were constructed in Seorak mountain area, Korea, using an integration of frequency ratio and adaptive neuro-fuzzy inference system (ANFIS) in geographical information system (GIS) environment. Landslide occurrence areas were detected in the study by interpreting aerial photographs and field survey data. Landslide locations were randomly selected in a 50/50 ratio for training and validation of the models, respectively. Topography, geology, soil, and forest databases were also constructed. Maps relevant to landslide occurrence were assembled in a spatial database. Using the constructed spatial database, 17 landslide-related factors were extracted. The adaptive ANFIS model with different types of membership functions (MFs) was then applied for landslide susceptibility mapping in the study area. Two landslide susceptibility maps were prepared using the different MFs. The frequency ratio model was also applied to the landslide susceptibility mapping for comparing with the probabilistic ANFIS model. Finally, the resulting landslide susceptibility maps were validated using the landslide locations which were not used for training the ANFIS. The validation results showed 75.57 % accuracy using the generalized bell-shaped MF model, 74.94 % accuracy using the Sigmoidal 2 MF model and 73.07 % accuracy using frequency ratio model. These accuracy results show that an ANFIS can be an effective tool in landslide susceptibility mapping.

Keywords

Integration Frequency ratio Adaptive neuro-fuzzy inference system (ANFIS) Landslide GIS Korea 
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Notes

Acknowledgments

This research was supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Ministry of Science, ICT and Future Planning, and Korea Environmental Institute (KEI) funded by the Development Project of Environmental Technology for Climate Change by the Korea Environmental Industry and Technology Institute.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Korea Environment InstituteSejongRepublic of Korea
  2. 2.Department of GeoinformaticsUniversity of SeoulSeoulRepublic of Korea
  3. 3.Geological Research DivisionKorea Institute of Geoscience and Mineral Resources (KIGAM)DaejeonRepublic of Korea
  4. 4.Korea University of Science and TechnologyDaejeonRepublic of Korea

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