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 proles into ten predened
templates, which may be used as an indicator whether potential avalanches might carry o
deeper layers of snow to form avalanches of higher magnitude. For this purpose, the snow
hardness prole 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 classication system.
Furthermore, as a secondary strategy, the class-templates themselves are modied in a neat
way that does not distort their overall shape, thereby providing matching candidates for a
larger portion of input proles. Altogether, the proposed system converts the input prole
into the internal representation, takes each of the modied 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.