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

Debora Gil*, Aura Hernàndez-Sabaté, Julien Enconniere, Saryani Asmayawati, Pau Folch, Juan Borrego-Carazo, Miquel Angel Piera

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)7489-7503
Number of pages15
JournalIEEE Access
Publication statusPublished - 2022


  • Decision support systems
  • Hard landing prediction
  • Machine learning
  • Neural networks


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