@Article{DeboraGil2022,
author="Debora Gil
and Aura Hernandez-Sabate
and Julien Enconniere
and Saryani Asmayawati
and Pau Folch
and Juan Borrego-Carazo
and Miquel Angel Piera",
title="E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights",
journal="IEEE Access",
year="2022",
volume="10",
pages="7489--7503",
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.",
optnote="IAM; 600.139; 600.118; 600.145",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3721), last updated on Tue, 25 Apr 2023 10:33:34 +0200",
doi="10.1109/ACCESS.2021.3138167"
}