Simulation-Based Prediction of Departure Aircraft Performance from Surveillance-like Data Using Neural Networks

Abstract

Estimating the environmental impact of aviation, in terms of noise and emissions, requires accurate knowledge of flight parameters such as departure/arrival procedure, aircraft mass, thrust and flap settings. However, these parameters are not available from radar/ADS-B data, which are the main sources of multiple flight-specific information including aircraft position over time. This paper introduces a novel approach to estimating these parameters from radar/ADS-B data using three neural network models trained on flight simulator data for the Boeing 747-400 airplane. The models are tested on 2204 simulated flights that are not used for training and that are transformed into radar/ADS-B data formats. The models are capable of predicting the unknown parameters for the ADS-B data format, with the weight prediction error being 2.63% of maximum takeoff weight, the average R2 score for the thrust profile prediction being 89.90% and the flap setting profile having an average R2 score of 84.68%, while for the radar-like data format, the values are 2.11%, 95.74% and 88.24%, respectively. The predicted parameters can be used to improve the environmental impact assessment of individual flights and to support policy-making and management decisions. This approach is a proof of concept based on simulation data that will need to be validated on real data before being applied in practice.