Avalanche forecasting is a complex process involving the assimilation
of multiple data sources to make predictions over varying spatial
and temporal resolutions. Numerically assisted forecasting often
uses nearest neighbour methods (NN), which are known to have limitations
when dealing with high dimensional data. We apply Support Vector
Machines to a dataset from Lochaber, Scotland to assess their applicability
in avalanche forecasting. Support Vector Machines (SVMs) belong to
a family of theoretically based techniques from machine learning
and are designed to deal with high dimensional data. Initial experiments
showed that SVMs gave results which were comparable with NN for categorical
and probabilistic forecasts. Experiments utilising the ability of
SVMs to deal with high dimensionality in producing a spatial forecast
show promise, but require further work.