Modeling monthly and seasonal Michigan snowfall based on machine learning: A multiscale approach

Abstract

Snowfall has important significance in water resources management and disaster prevention worldwide. Accurate prediction of both mean and extreme snowfall is challenging because of multiple controlling mechanisms at different spatial and temporal scales. By using a 65 years long in-situ snowfall observation, we evaluated seven different machine learning algorithms for predicting monthly snowfall in the Lower Peninsula of Michigan (LPM). The Bayesian Additive Regression Trees (BART) demonstrates the best fitting (R2 = 0.88) and out-of-sample prediction skills (R2 = 0.58) for the monthly mean snowfall followed by the Random Forest model. The BART also demonstrate strong predictive skills for seasonal and the extreme