More than half of all commercial aircraft operation accidents could have been prevented by executing a go-around.Making timely decision to execute a go-around Fridge Water Filter Receptacle manoeuvre can potentially reduce overall aviation industry accident rate.In this paper, we describe a cockpit-deployable machine learning system to support flight crew go-around decision-making based on the prediction of a hard landing event.This work presents a hybrid approach for hard landing prediction 15-20mmHg that uses features modelling temporal dependencies of aircraft variables as inputs to a neural network.
Based on a large dataset of 58177 commercial flights, the results show that our approach has 85% of average sensitivity with 74% of average specificity at the go-around point.It follows that our approach is a cockpit-deployable recommendation system that outperforms existing approaches.