Open Machine Learning Models for Actual Takeoff Weight Prediction

Abstract

Aircraft weight is a key input in flight trajectory prediction and environmental impact assessment tools. However, the lack of openly available data regarding the actual aircraft weight throughout the flight requires the development of mass estimation approaches to be incorporated into these tools. This study uses large-scale open aviation data made available by Eurocontrol's Performance Review Commission to develop an open-source machine learning model to predict commercial flights' actual takeoff weight. The data combines detailed flight, trajectory, and meteorological information for 369,013 flights that transited through the European airspace in 2022. Several operational features are created to represent each flight's horizontal and vertical profiles accurately. For model learning, we employ CatBoost, LightGBM, XGBoost, artificial neural networks, and an ensemble of these models, which were selected for their robust performance in structured data analysis and potential for high predictive accuracy. The models are evaluated based on their efficiency, accuracy, and applicability to real-world data. The best-performing model is found to predict the aircraft takeoff weights with a mean percentage of error of 1.73%.