E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights

E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights

Blog Article

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 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 ALOE VERA LIP BALM decision-making based on the prediction of a hard landing event.

This work presents a hybrid approach for hard landing prediction that uses features modelling temporal dependencies of aircraft variables as inputs to a Trailer Parts and Accessories 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.