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

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 Crown Copyright (C) 2010 Published by Elsevier BV All rights reserved