Pozdnoukhov, A. and Matasci, G. and Kanevski, M. and Purves, R. S.
Spatio-temporal avalanche forecasting with Support Vector
Natural Hazards and Earth System Sciences, 11 (2).
This paper explores the use of the Support Vector
Machine (SVM) as a data exploration tool and a predictive
engine for spatio-temporal forecasting of snow avalanches.
Based on the historical observations of avalanche activity,
meteorological conditions and snowpack observations in the
field, an SVM is used to build a data-driven spatio-temporal
forecast for the local mountain region. It incorporates the
outputs of simple physics-based and statistical approaches
used to interpolate meteorological and snowpack-related
data over a digital elevation model of the region. The
interpretation of the produced forecast is discussed, and
the quality of the model is validated using observations
and avalanche bulletins of the recent years. The insight
into the model behaviour is presented to highlight the
interpretability of the model, its abilities to produce reliable forecasts for individual avalanche paths and sensitivity to input data. Estimates of prediction uncertainty are obtained with ensemble forecasting. The case study was carried out using data from the avalanche forecasting service in the Locaber region of Scotland, where avalanches are forecast on a daily basis during the winter months.
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