Impact of model choice in predicting urban forest storm damage when data is uncertain

•Choice of method for handling uncertain data affected model accuracy and drivers.•Random Forests (RF) and Generalized Linear Models (GLM) had similar accuracy.•RF and GLM identified different drivers of storm damage.•Imputing uncertain observations resulted in lower accuracy than excluding them.•GLMs showed greater variation than RF when damage proportion was altered.