TY - JOUR AU - Debora Gil AU - Aura Hernandez-Sabate AU - Julien Enconniere AU - Saryani Asmayawati AU - Pau Folch AU - Juan Borrego-Carazo AU - Miquel Angel Piera PY - 2022// TI - E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights T2 - ACCESS JO - IEEE Access SP - 7489 EP - 7503 VL - 10 N2 - 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. UR - http://dx.doi.org/10.1109/ACCESS.2021.3138167 N1 - IAM; 600.139; 600.118; 600.145 ID - Debora Gil2022 ER -