IJMLC 2019 Vol.9(1): 1-7 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.1.1-7

Airplane Vortex Encounters Identification Using Multilayer Feed-Forward Neural Networks

Faouzi Bouslama and Aziz Al-Mahadin

Abstract—The encounter of vortices generated by a leading aircraft during takeoff or landing can be a source of hazard to a following aircraft. In spite of airport efforts to keep safe separation distances between aircrafts, a number of them encounter severe vortices each year. It has been challenging to accurately identify those encounters by manual approaches. To mitigate the impact of vortex encounters on an aircraft, it is important that more reliable identification techniques be developed. This research is a contribution towards the automatic identification of vortex encounters using artificial neural networks. Multilayer feedforward neural networks are trained using the back-propagation learning algorithm to classify flight events into either vortex encounters or other events. Using salient inputs such as aircraft roll angle, normal acceleration and lateral acceleration, the neural networks are able to achieve an overall average identification rate of about 88%. These results confirm the authors’ earlier assumption on using a reduced set of critical inputs to properly classify aircraft vortex encounters.

Index Terms—Vortex encounter, flight data recorder (FDR), neural networks (NN), multilayer feed-forward (MLFF) network.

Faouzi Bouslama is with the CIS Department, Dubai Men’s College, the Higher Colleges of Technology, PO Box 15825, Dubai, UAE (e-mail: faouzi.bouslama@hct.ac.ae).
Aziz Al-Mahadin is with the Aviation Engineering Department, Dubai Men’s College, the Higher Colleges of Technology, PO Box 15825, Dubai, UAE (e-mail: aziz.almahadin@hct.ac.ae).

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Cite: Faouzi Bouslama and Aziz Al-Mahadin, "Airplane Vortex Encounters Identification Using Multilayer Feed-Forward Neural Networks," International Journal of Machine Learning and Computing vol. 9, no. 1, pp. 1-7, 2019.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net