Assessing susceptibility to landslides: Using models to understand observed changes in slopes
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A map of landslide susceptibility is a necessary tool for proper planning and selection of sites for agriculture, infrastructure and other human developments. The Paonia-McClure Pass area of Colorado, USA, is well known for active mass movements. Large losses of property and risks to people highlight the need to accurately map susceptibility to shallow landslides and to identify safe locations for infrastructure and residential development.We mapped 735 active mass movements and 17 factors about each one. The weights of evidence, frequency ratio of landslides, and fuzzy-logic method were used to create an optimum map of landslide susceptibility. Weights of the evidence were used to categorize continuous factor data, frequency ratios of shallow landslides were used to assign the membership values for the categories of the factors, and the fuzzy-logic method was used to integrate the membership values. Four models from the fuzzy-inference network of mapping susceptibility to shallow landslides were developed based on the combination of factors using five types of fuzzy operators. The first inference network model was comprised of the combination of factors, which are independent of each other. The second, third and the fourth inference network models were developed such that factors are not necessarily independent of each other. These models combine all dependent and independent factors, based on the expert's knowledge. Intermediate steps in the second, third and fourth models were developed by combining the fuzzy factors in the first step by fuzzy-OR, fuzzy-AND, and fuzzy-OR plus fuzzy-AND operations, respectively.All models predicted similar percentages of observed shallow landslides with the fuzzy-gamma operation. Although the prediction capabilities of all the models are not significantly different, the fourth model is the best because it is the only model that accommodates the under-sampled and missed landslide data and the effect of increasing and decreasing gamma values. The first and third models create a problem if a category of a factor has a 0 membership value because of the absence or under-sampling of shallow landslides. The second model incurs the highest increasing effect of gamma values, and the third model incurs the highest decreasing effect of gamma values. The approaches described in this paper reduce the uncertainties associated with the categorization of continuous data, determination of fuzzy-membership values, and the combination of factors that causes shallow landslides. 2010 Elsevier B.V.
