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http://dx.doi.org/10.7780/kjrs.2010.26.1.47

Landslide Detection and Landslide Susceptibility Mapping using Aerial Photos and Artificial Neural Networks  

Oh, Hyun-Joo (Korea Institute of Geoscience and Mineral Resources (KIGAM))
Publication Information
Korean Journal of Remote Sensing / v.26, no.1, 2010 , pp. 47-57 More about this Journal
Abstract
The aim of this study is to detect landslide using digital aerial photography and apply the landslide to landslide susceptibility mapping by artificial neural network (ANN) and geographic information system (GIS) at Jinbu area where many landslides have occurred in 2006 by typhoon Ewiniar, Bilis and Kaemi. Landslide locations were identified by visual interpretation of aerial photography taken before and after landslide occurrence, and checked in field. For landslide susceptibility mapping, maps of the topography, geology, soil, forest, lineament, and landuse were constructed from the spatial data sets. Using the factors and landslide location and artificial neural network, the relative weight for the each factors was determinated by back-propagation algorithm. As the result, the aspect and slope factor showed higher weight in 1.2-1.5 times than other factors. Then, landslide susceptibility map was drawn using the weights and finally, the map was validated by comparing with landslide locations that were not used directly in the analysis. As the validation result, the prediction accuracy showed 81.44%.
Keywords
Landslide susceptibility; Geographic information system; Artificial neural network; Weight; Aerial photography; Jinbu; GIS; ANN;
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Times Cited By KSCI : 2  (Citation Analysis)
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