Aircraft takeoff speed prediction with deep learning: a comparative study of MLP, 1D-CNN, LSTM and attention-based architectures on Boeing 737-300 data

Abstract

Modern aviation supports an ever-broader range of civil and military missions, and the airframes designed for these missions must satisfy stringent safety and performance requirements. The takeoff and landing phases are the most accident-prone portions of a flight despite representing only a short interval of the total block time, which makes the accurate prediction of takeoff speed a safety-relevant problem. A previous machine learning study addressed the takeoff-speed prediction problem of the Boeing 737-300 with classical regressors using pressure altitude, outside air temperature, gross weight and flap angle as the predictors. In the present work, the same regression problem is revisited under the deep learning paradigm. Four neural architectures are trained on an identical pre-processing pipeline and train-validation partition, namely a multilayer perceptron, a one-dimensional convolutional network, a long short-term memory network and a wide-and-deep architecture incorporating multi-head self-attention. Among the four candidates, the long short-term memory network attains the lowest root mean square error and mean square error on the unseen test file and is subsequently subjected to Bayesian hyperparameter optimisation through the Keras Tuner library. The predicted and the measured takeoff speeds are reported side by side for the first time in the deep learning literature for this airframe, and the simulation results indicate that the developed networks constitute an effective alternative tool for takeoff-speed prediction.