Item: Use of neural networks in avalanche hazard forecasting
Title: Use of neural networks in avalanche hazard forecasting
Proceedings: Proceedings of the 1994 International Snow Science Workshop, Snowbird, Utah, USA
Authors: J. Stephens, E. Adams, X. Huo, J. Dent, J. Hicks and D. MCCarty
Abstract: Artificial neural networks were investigated as a tool to be used in avalanche hazard forecasting. Such forecasts are presently formulated by experts using their knowledge and experience. While the experts utilize a variety of information in their decision making process, the exact nature of the relationship between these factors and the level of avalanche hazard is unknown. The consistency and reliability with which these assessments are made may be improved, if the experts are provided with tools, such as an artificial neural networlc, that explicity utilize these various types of information. Trial networks were developed herein that act upon weather and snow condition data to generate an opinion on avalanche activity on a daily basis for a specific slide path. These networks were developed using historical avalanche data. Networks trained on a season by season basis issued correct predictions of avalanche activity as many as 78 to 91 percent of the evaluation cases considered. On days that avalanches were known to occur, these networks were correct for 36 to 100 percent of the cases considered. Networks trained over multiple seasons were less successful, correctly predicting avalanche activity for up to 82 percent of the days considered, but with a success rate on days avalanches were known to occur of only 40 percent. Additional work is necessary before a tool of this type will be useful to the avalanche expert. Improvements in network performance may. result from modifying inputs, and/or modifying the newtwork architecture, training algorithm, or output configuration.
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Keywords: avalanche activity, avalanche forecasting, avalanche hazard, neural networks
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