Citation

Arnaldo Valdés RM, Liang Cheng SZY, Gómez Comendador VF, Sáez Nieto FJ. Entropy (Basel) 2018; 20(12): e20120969.

Copyright

(Copyright © 2018, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/e20120969

PMID

33266693

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.

Language: en

Keywords

entropy; aviation safety; Bayesian network approach; information theory; loss of separation