Abstract
Statistical classification techniques complemented by the use of GIS have been shown to yield good results at the task of an assessment of landslide hazard/ susceptibility. In this work, several classification methods previously applied to this task are compared with respect to their performance on data sampled from distinct alpine areas in Vorarlberg, Austria. It is shown that among different types of techniques, kernel methods, including the Support Vector Machine and the Gaussian Process model, outperform techniques traditionally employed for the task. As further result, hazard maps for the study areas are generated, which can be used as input for suitable early warning systems focussing on landslide hazard.
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Gallus, D., Abecker, A., Richter, D. (2008). Classification of Landslide Susceptibility in the Development of Early Warning Systems. In: Ruas, A., Gold, C. (eds) Headway in Spatial Data Handling. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68566-1_4
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DOI: https://doi.org/10.1007/978-3-540-68566-1_4
Publisher Name: Springer, Berlin, Heidelberg
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