The talk starts at 12:15.

Please note that due to COVID-19, the participants can watch the streamed talk on Teams with a link (below).

Speaker: Alise Danielle Midtfjord from UiO

Alise did her Master of Science at the Norwegian University of Life Sciences (NMBU) in Environmental Physics. After that, she worked with ML as a technology consulting for Accenture, before she became a Ph.D. Student at the Department of Mathematics at the University of Oslo fall 2019 under the supervision of Arne Bang Huseby. She is currently working on modeling and analysis of multidimensional high-resolution environmental data within safety-critical systems such as airport runway condition management.

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Title: A Machine Learning Approach to Safer Airplane Landings

Abstract:

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 predict slippery conditions and a regression model to predict the level of slipperiness. The models are trained on weather data and data from runway reports. The runway surface conditions are represented by the tire-pavement friction coefficient, which is estimated from flight sensor data from landing aircrafts. To evaluate the performance of the 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 an MAE of 0.0254, and outperform all the previous methods. The results show the strong abilities of machine learning methods to model complex, physical phenomena with good accuracy when domain knowledge is used in the variable extraction. The XGBoost models are combined with SHAP (SHapley Additive exPlanations) approximations to provide a comprehensible decision support system for airport operators and pilots, which can contribute to safer and more economic operations of airport runways.