Treed Avalanche Forecasting: Mitigating Avalanche Danger Utilizing Bayesian Additive Regression Trees

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• 作者：Gail Blattenberger and Richard Fowles
• 刊名：Journal of Forecasting
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：36
• 期：2
• 页码：165-180
• 全文大小：1710K
• ISSN：1099-131X

文摘

Little Cottonwood Canyon Highway is a dead-end, two-lane road leading to Utah's Alta and Snowbird ski resorts. It is the only road access to these resorts and is heavily traveled during the ski season. Professional avalanche forecasters monitor this road throughout the ski season in order to make road closure decisions in the face of avalanche danger. Forecasters at the Utah Department of Transportation (UDOT) avalanche guard station at Alta have maintained an extensive daily winter database on explanatory variables relating to avalanche prediction. Whether or not an avalanche crosses the road is modeled in this paper via Bayesian additive tree methods. Utilizing daily winter data from 1995 to 2011, results show that using Bayesian tree analysis outperforms traditional statistical methods in terms of realized misclassification costs that take into consideration asymmetric losses arising from two types of error. Closing the road when an avalanche does not occur is an error harmful to resort owners, and not closing the road when one does may result in injury or death. Copyright