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

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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.
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Document Type: Research Article

Affiliations: 1: Department of Geography San Diego State University 2: Department of Forest Resources University of Idaho

Publication date: 2005-10-01

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