Entropy 2018, 20(12), 969; https://doi.org/10.3390/e20120969
Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors
Rosa María Arnaldo Valdés 1,*
, Schon Z.Y. Liang Cheng 1,2
, Victor Fernando Gómez Comendador 1 and Francisco Javier Sáez Nieto 3
, Victor Fernando Gómez Comendador 1 and Francisco Javier Sáez Nieto 3
1
Department of Sistemas Aeroespaciales, Transporte Aéreo y Aeropuertos, School of Aerospace Engineering, Universidad Politécnica de Madrid (UPM), Plaza Cardenal Cisneros n3, 28040 Madrid, Spain
2
Aeronautic, Space & Defence Division, ALTRAN Innovation S.L., Calle Campezo 128022 Madrid, Spain
3
Centre for Aeronautics, School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 OAL, UK
*
Author to whom correspondence should be addressed.
Received: 12 November 2018 / Revised: 8 December 2018 / Accepted: 11 December 2018 / Published: 14 December 2018
(This article belongs to the Special Issue Bayesian Inference and Information Theory)
Abstract
This paper combines Bayesian networks (BN) and information theory to model the likelihood of severe loss of separation (LOS) near accidents, which are considered mid-air collision (MAC) precursors. BN is used to analyze LOS contributing factors and the multi-dependent relationship of causal factors, while Information Theory is used to identify the LOS precursors that provide the most information. The combination of the two techniques allows us to use data on LOS causes and precursors to define warning scenarios that could forecast a major LOS with severity A or a near accident, and consequently the likelihood of a MAC. The methodology is illustrated with a case study that encompasses the analysis of LOS that have taken place within the Spanish airspace during a period of four years. View Full-TextKeywords:
aviation safety; loss of separation; Bayesian network approach; information theory; entropy
▼
Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Share & Cite This Article
MDPI and ACS Style
Arnaldo Valdés, R.M.; Liang Cheng, S.Z.; Gómez Comendador, V.F.; Sáez Nieto, F.J. Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors. Entropy 2018, 20, 969.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.