Using Linear Discriminant Analysis to Classify Snowfall Situations into Avalanching and Non-Avalanching Ones

Abstract

AbstractThe existing methods of predicting avalanche danger often do not meet users’ demands because of the empiric character of the insufficient volume of information used. In such forecasts the contribution of each individual parameter into the prognostic information is unknown, and this is very important when studying such an event as avalanche formation, which is conditioned by a complex interaction of numerous factors, including snow accumulation, snow thickness, and the conditions of its development. It is obvious that such problems can be successfully solved by statistical methods, and that explains the growing interest in numerical methods of avalanche forecasting. Problems of multi-dimensional observations arises in many scientific fields. The method suited for this problem is discriminant analysis, the purpose of which is to divide a multi-dimensional observation vector into predetermined classes.This study considers the prognostic (diagnostic) problems of fresh-snow avalanches released during snowfall or in the two days after it has ceased. The theoretical basis is a complex of statistical methods: correlation and dispersion analysis, “sifting” for the choice of predictors’ informative groups, construction of linear parametric discriminant functions, predictions based on training sample, and verification of discriminant functions based on independent material.The archive used in the study consisted of 500 avalanching cases and 1 300 non-avalanching ones. All situations were grouped according to geomorphological characteristics. Each situation is described by eight meteorological characteristics. The results of classification of snowfall situations into avalanching and non-avalanching ones are as follows: reliability ofpis from 75% to 91%,Hfrom 0.15 to 0.51; based on independent material the reliability ofpis from 63% to 85%,Hfrom 0. 10 to 0.56.