Spatial Prediction of Landslide Hazard Using Fuzzy k-means and Dempster-Shafer Theory

Abstract

Landslide databases and input parameters used for modeling landslide hazard often contain imprecisions and uncertainties inherent in the decision-making process. Dealing with imprecision and uncertainty requires techniques that go beyond classical logic. In this paper, methods of fuzzy k-means classification were used to assign digital terrain attributes to continuous landform classes whereas the Dempster-Shafer theory of evidence was used to represent and manage imprecise information and to deal with uncertainties. The paper introduces the integration of the fuzzy k-means classification method and the Dempster-Shafer theory of evidence to model landslide hazard in roaded and roadless areas illustrated through a case study in the Clearwater National Forest in central Idaho, USA. Sample probabilistic maps of landslide hazard potential and uncertainties are presented. The probabilistic maps are intended to help decision-making in effective forest management and planning.