Determining avalanche activity corresponding to given snow and meteorological conditions is an old problem of high practical relevance. To address it, numerous statistical forecasting models have been developed, but intercomparisons of their efficiency on very large datasets are seldom. In this work, an approach combining random forests with class-balancing is presented and systematically compared with competing methods currently described in the avalanche literature. On more than 50 years of daily avalanche observations, in the 23 massifs of the French Alps, the competing classifiers are evaluated on their ability to distinguish three classes of avalanche activity: non-avalanche days, days with moderate activity, and days with high activity. Moreover, the variables of higher importance in the random forest classifiers are shown to be coherent with current avalanche literature and a clustering based on these variable importance separates massifs which are known to have different avalanche activities. Our approach opens perspectives to support operational avalanche forecasting.

确定与给定的雪和气象条件相对应的雪崩活动是一个具有高度实际意义的老问题。为了解决这个问题，已经开发了许多统计预测模型，但是很少在大型数据集上进行效率比较。在这项工作中，提出了一种将随机森林与类平衡相结合的方法，并与雪崩文献中当前描述的竞争方法进行了系统地比较。在超过50年的每日雪崩观测中，在法国阿尔卑斯山的23个断层中，对竞争分类器进行区分三类雪崩活动的能力的评估：非雪崩天，中等活动天和高活动天。 。而且，结果表明，在随机森林分类器中具有更高重要性的变量与当前的雪崩文献是一致的，并且基于这些变量重要性的聚类将已知具有不同雪崩活动的地块分开。我们的方法为支持运营雪崩预测开辟了前景。