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Weight Determination of Landslide Factors Using Artificial Neural Networks  

류주형 (연세대학교 지구시스템과학과)
이사로 (한국지질자원연구원 국가지질자원정보센터)
원중선 (연세대학교 지구시스템과학과)
Publication Information
Economic and Environmental Geology / v.35, no.1, 2002 , pp. 67-74 More about this Journal
Abstract
The purpose of this study is to determine the weights of the factors for landslide susceptibility analysis using artificial neural network. Landslide locations were identified from interpretation of aerial photographs, field survey data, and topography. The landslide-related factors such as topographic slope, topographic curvature, soil drainage, soil effective thickness, soil texture, wood age and wood diameter were extracted from the spatial database in study area, Yongin. Using these factors, the weights of neural networks were calculated by backpropagation training algorithm and were used to determine the weight of landslide factors. Therefore, by interpreting the weights after training, the weight of each landslide factor can be ranked based on its contribution to the classification. The highest weight is topographic slope that is 5.33 and topographic curvature and soil texture are 1 and 1.17, respectively. Weight determination using backprogpagation algorithms can be used for overlay analysis of GIS so the factor that have low weight can be excluded in future analysis to save computation time.
Keywords
GIS; andslide; artificial neural network; backpropagaion algorithm; weight determination;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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