Item: Estimating the Forecasting Success of Artificially Triggering of Avalanches with the Combination of Cluster and Discriminant Analysis
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Title: Estimating the Forecasting Success of Artificially Triggering of Avalanches with the Combination of Cluster and Discriminant Analysis
Proceedings: International Snow Science Workshop, Davos 2009, Proceedings
Authors:
- Reinhard Fromm [ Department Natural Hazards and Alpine Timberline, Federal Research and Training Centre for Forest, Natural Hazards and Landscape, Austria ]
Date: 2009
Abstract: Meteorological conditions play an important role for the mechanical stability of the snowpack. Statistical algorithms can be used to determine the likelihood of the occurrence of avalanches which was shown in several previous studies. The common use of two statistical methods offers the possibility to analyze the data set in regard to specific weather conditions. In the first step the k-mean cluster analysis selects days with similar weather conditions; so that each day is assigned to a predefined group. Significant weather conditions are used for the definition of the initial conditions of each cluster. Each group represents a typical weather situation. In the second step the discriminant analysis is used to separate between avalanche and non-avalanche days for each group. Consequently the coefficients of the discriminant functions can differ and the best fit of the discriminant function depending on the predominant weather situation is applied. Results of the clustering algorithm and the hit rate of the subsequent discriminant analysis are shown.
Object ID: issw-2009-0366-0370.pdf
Language of Article: English
Presenter(s): Unknown
Keywords: avalanche forecast, cluster analysis, discriminant analysis
Page Number(s): 366-370
Subjects: artificially triggering avalanches cluster and discriminant analysis
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