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The Landslide Probability Analysis using Logistic Regression Analysis and Artificial Neural Network Methods in Jeju  

Quan, He Chun (중국 연변대학교 토목공학과)
Lee, Byung-Gul (제주대학교 해양과학대학 토목공학과)
Lee, Chang-Sun (제주대학교 대학원 토목해양공학과)
Ko, Jung-Woo (제주대학교 대학원 통역특성화협동과정)
Publication Information
Journal of Korean Society for Geospatial Information Science / v.19, no.3, 2011 , pp. 33-40 More about this Journal
Abstract
This paper presents the prediction and evaluation of landslide using LRA(logistic regression analysis) and ANN (Artificial Neural Network) methods. In order to assess the landslide, we selected Sarabong, Byeoldobong area and Mt. Song-ak in Jeju Island. Five factors which affect the landslide were selected as: slope angle, elevation, porosity, dry density, permeability. So as to predict and evaluate the landslide, firstly the weight value of each factor was analyzed by LRA(logistic regression analysis) and ANN(Artificial Neural Network) methods. Then we got two prediction maps using AcrView software through GIS(Geographic Information System) method. The comparative analysis reveals that the slope angle and porosity play important roles in landslide. Prediction map generated by LRA method is more accurate than ANN method in Jeju. From the prediction map, we found that the most dangerous area is distributed around the road and path.
Keywords
LRA; ANN; GIS; Prediction Map;
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Times Cited By KSCI : 3  (Citation Analysis)
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