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| Titel |
Spatio-temporal avalanche forecasting with Support Vector Machines |
| VerfasserIn |
A. Pozdnoukhov, G. Matasci, M. Kanevski, R. S. Purves |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 11, no. 2 ; Nr. 11, no. 2 (2011-02-09), S.367-382 |
| Datensatznummer |
250009153
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| Publikation (Nr.) |
 copernicus.org/nhess-11-367-2011.pdf |
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| Zusammenfassung |
| 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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