%0 Journal Article
%T E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights
%A Debora Gil
%A Aura Hernandez-Sabate
%A Julien Enconniere
%A Saryani Asmayawati
%A Pau Folch
%A Juan Borrego-Carazo
%A Miquel Angel Piera
%J IEEE Access
%D 2022
%V 10
%F Debora Gil2022
%O IAM; 600.139; 600.118; 600.145
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3721), last updated on Tue, 25 Apr 2023 10:33:34 +0200
%X 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.
%U http://dx.doi.org/10.1109/ACCESS.2021.3138167
%P 7489-7503