A Comparative Study of Fuzzy Relationship and ANN for Landslide Susceptibility in Pohang Area

퍼지관계 기법과 인공신경망 기법을 이용한 포항지역의 산사태 취약성 예측 기법 비교 연구

  • Kim, Jin Yeob (Department of Geoinformation Engineering, Sejong University) ;
  • Park, Hyuck Jin (Department of Geoinformation Engineering, Sejong University)
  • 김진엽 (세종대학교 지구정보공학과) ;
  • 박혁진 (세종대학교 지구정보공학과)
  • Received : 2013.05.07
  • Accepted : 2013.08.02
  • Published : 2013.08.28


Landslides are caused by complex interaction among a large number of interrelated factors such as topography, geology, forest and soils. In this study, a comparative study was carried out using fuzzy relationship method and artificial neural network to evaluate landslide susceptibility. For landslide susceptibility mapping, maps of the landslide occurrence locations, slope angle, aspect, curvature, lithology, soil drainage, soil depth, soil texture, forest type, forest age, forest diameter and forest density were constructed from the spatial data sets. In fuzzy relation analysis, the membership values for each category of thematic layers have been determined using the cosine amplitude method. Then the integration of different thematic layers to produce landslide susceptibility map was performed by Cartesian product operation. In artificial neural network analysis, the relative weight values for causative factors were determined by back propagation algorithm. Landslide susceptibility maps prepared by two approaches were validated by ROC(Receiver Operating Characteristic) curve and AUC(Area Under the Curve). Based on the validation results, both approaches show excellent performance to predict the landslide susceptibility but the performance of the artificial neural network was superior in this study area.


fuzzy relationship;artificial neural network;landslide susceptibility;fuzzy membership value;Pohang


Supported by : 한국연구재단


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