  The presence of snow and ice on runway surfaces reduces the available
tire-pavement friction needed for retardation and directional control and
causes potential economic and safety threats for the aviation industry during
the winter seasons. To activate appropriate safety procedures, pilots need
accurate and timely information on the actual runway surface conditions. In
this study, XGBoost is used to create a combined runway assessment system,
which includes a classification model to identify slippery conditions and a
regression model to predict the level of slipperiness. The models are trained
on weather data and runway reports. The runway surface conditions are
represented by the tire-pavement friction coefficient, which is estimated from
flight sensor data from landing aircrafts. The XGBoost models are combined with
SHAP approximations to provide a reliable decision support system for airport
operators, which can contribute to safer and more economic operations of
airport runways. To evaluate the performance of the prediction models, they are
compared to several state-of-the-art runway assessment methods. The XGBoost
models identify slippery runway conditions with a ROC AUC of 0.95, predict the
friction coefficient with a MAE of 0.0254, and outperforms all the previous
methods. The results show the strong abilities of machine learning methods to
model complex, physical phenomena with a good accuracy. Published version:
https://doi.org/10.1016/j.coldregions.2022.103556.

Computers and Society;Machine Learning;Applications