Browse > Article

Landslide Susceptibility Analysis and Vertification using Artificial Neural Network in the Kangneung Area  

Lee, Sa-Ro (Geoscience Information Center, Korea Institute of Geoscience & Mineral Resources (KIGAM))
Lee, Myeong-Jin (Department of Earth System Science, Yonsei University)
Won, Jung-Seon (Department of Earth System Science, Yonsei University)
Publication Information
Economic and Environmental Geology / v.38, no.1, 2005 , pp. 33-43 More about this Journal
Abstract
The purpose of this study is to make and validate landslide susceptibility map using artificial neural network and GIS in Kangneung area. For this, topography, soil, forest, geology and land cover data sets were constructed as a spatial database in GIS. From the database, slope, aspect, curvature, water system, topographic type, soil texture, soil material, soil drainage, soil effective thickness, wood type, wood age, wood diameter, forest density, lithology, land cover, and lineament were used as the landslide occurrence factors. The weight of the each factor was calculated, and applied to make landslide susceptibility maps using artificial neural network. Then the maps were validated using rate curve method which can predict qualitatively the landslide occurrence. The landslide susceptibility map can be used to reduce associated hazards, and to plan land use and construction as basic data.
Keywords
GIS; Landslide; Susceptibility; Artificial Neural Network; Kangneung;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Lee, S., Ryu, J-H., Min, K.D. and Won, J-S. (2003b) Landslide Susceptibility Analysis using GIS and Artificial neural network. Earth Surface Processes and Landforms, v. 27. p. 1361-1376
2 Lee, S., Ryu, J-H., Lee, M-J. and Won, J-S. (2003a) Landslide susceptibility analysis using artificial neural network at Boun, Korea. Environmental Geology, v. 44, p.820-833   DOI   ScienceOn
3 Lee, S., Ryu, J-H., Won, J-S. and Park H-J. (2004) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Engineering Geology, v. 71. p. 289-302   DOI   ScienceOn
4 류주형, 이사로, 원중선 (2002) 인공신경망을 이용한 산사태 발생요인의 가중치 결정. 자원환경지질학회지, v.35, 67-74
5 Hines, J.W. (1997) Fuzzy and Neural Approaches in Engineering. John Wiley and Sons, New York, 209p
6 이명진, 이사로, 원중선 (2004) GIS와 원격탐사를 이용한강릉지역 산사태 연구(I)-산사태 발생 위치와 영향인자와의 상관관계 분석. 자원환경지질학회지, v. 37, p.425-436
7 Paola, J.D. and Schowengerdt, R.A. (1995) A review and analysis of backpropagation neural networks for classification of remotely sensed multi-spectral imagery. International Journal of Remote Sensing, v. 16, p.3033-3058   DOI   ScienceOn
8 이사로, 류주형, 민경덕, 원중선 (2000) 인공신경망을 이용한 산사태 취약성 분석. 자원환경지질학회지, v. 33, p. 333-340