Spatio-temporal avalanche forecasting with Support Vector Machines

Pozdnoukhov, A. and Matasci, G. and Kanevski, M. and Purves, R. S. (2011) Spatio-temporal avalanche forecasting with Support Vector Machines. Natural Hazards and Earth System Sciences, 11 (2). pp. 367-382. ISSN 1561-8633

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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.

Item Type: Article
Additional Information: © Author(s) 2011. Creative Commons Attribution 3.0 License. The original article is available at doi:10.5194/nhess-11-367-2011
Keywords: Spatio-temporal; avalanche forecasting; Support Vector Machine; predicition;
Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
Item ID: 3930
Depositing User: Dr Alexei Pozdnoukhov
Date Deposited: 04 Oct 2012 08:48
Journal or Publication Title: Natural Hazards and Earth System Sciences
Publisher: European Geosciences Union (EGU)
Refereed: Yes

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