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
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: firstname.lastname@example.org).
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: email@example.com).
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.