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

Abstract

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 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 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.INDEX TERMS Decision support systems, hard landing prediction, machine learning, neural networks.

An advanced warning model of airplane hard landing based on Adaboost

“…Under the condition that the mean square error is used as the precision index, its precision level is higher than the current related research. Gil [5] described a cockpit-deployable machine learning system to support flight crew go-around decisionmaking based on the prediction of a hard landing event. This work presents a hybrid approach for hard landing prediction that uses features modeling temporal dependencies of aircraft variables as inputs to a neural network.…”

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