Development of Algorithms for the Automatic Analysis of Snow Profiles

Today avalanche research primarily focuses on the reliable prediction of avalanche descents by using meteorological data and snowpack properties. This thesis complements these approaches by providing means to automatically categorize snow proles into ten predened templates, which may be used as an indicator whether potential avalanches might carry on deeper layers of snow to form avalanches of higher magnitude. For this purpose, the snow
hardness prole is taken and transformed into a symbolic internal data format based on weighted strings. This representation allows for the application of well-known methods such as string alignments, which provide the foundation for the classication system. Furthermore, as a secondary strategy, the class-templates themselves are modied in a neat way that does not distort their overall shape, thereby providing matching candidates for a larger portion of input proles. Altogether, the proposed system converts the input prole into the internal representation, takes each of the modied template versions and determines
a proper alignment of hardness layers as the basis for a nal error score computation that enables an ordering among the contemplable template types. The work in this thesis may be generalized to approach a wider range of problems and is closely related to the eld of time series data mining.