Landslide Susceptibility Analysis Based on Data Field

The Three Gorges are the areas in which the geological disasters are very serious. There often happen great landslide disasters, which brings tremendous threat to normal running of the Three Gorge Dam and the properties and lives of the residents in the reservoir. So landslide susceptibility analysis is an important task of prevention and cure of landslides in the Three Gorges. In this paper, landslide susceptibility analysis in the Three Gorges is studied based on spatial data mining. ETM+ image, 1: 50000 geological graph and 1:10000 relief map are adopted as the data origins to produce the factors closely related to landslide transmutation, including slope structure, engineering rock group, slope level, fluctuation influence of reservoir water and land exploration. A spatial data mining method is proposed which is suitable for landslide susceptibility analysis. Firstly data field method is adopted to synthetically analyze the spatial distribution of landslides and the key factors influencing landslide transmutation and extract the potential centers. Secondly cloud model method is adopted to describe the concept represented by each potential center, and the synthesized cloud method elevates the concepts to produce the high-level concepts. Finally clustering analysis is made according to the membership degree of each data point to each high-level concept, and realizes landslide susceptibility analysis in the Three Gorges. The experimental results have shown that the method proposed in this paper obtains a good prediction result, which is priori to the ones of the other 3 methods (IsoData, K-Means and Parallelepiped). So the method can well realize landslide susceptibility analysis in the Three Gorges.

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