Landslide susceptibility mapping in Injae, Korea, using a decision tree

Abstract

A data mining classification technique can be applied to landslide susceptibility mapping. Because of its advantages, a decision tree is one popular classification algorithm, although hardly used previously to analyze landslide susceptibility because the obtained data assume a uniform class distribution whereas landslide spatial event data when represented on a grid raster layer are highly class imbalanced. For this study of South Korean landslides, a decision tree was constructed using Quinlan's algorithm C4.5. The susceptibility of landslide occurrence was then deduced using leaf-node ranking or m-branch smoothing. The area studied at Injae suffered substantial landslide damage after heavy rains in 2006. Landslide-related factors for nearly 600 landslides were extracted from local maps: topographic, including curvature, slope, distance to ridge, and aspect; forest, providing age, type, density, and diameter; and soil texture, drainage, effective thickness, and material. For the quantitative assessment of landslide susceptibility, the accuracy of the twofold cross-validation was 86.08%; accuracy using all known data was 89.26% based on a cumulative lift chart. A decision tree can therefore be used efficiently for landslide susceptibility analysis and might be widely used for prediction of various spatial events.