@article{ref1,
title="Application of Bayesian networks and information theory to estimate the occurrence  of mid-air collisions based on accident precursors",
journal="Entropy (Basel, Switzerland)",
year="2018",
author="Arnaldo Valdés, Rosa María and Liang Cheng, Schon Z. Y. and Gómez Comendador, Víctor Fernando and Sáez Nieto, Francisco Javier",
volume="20",
number="12",
pages="e20120969-e20120969",
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.<p /> <p>Language: en</p>",
language="en",
issn="1099-4300",
doi="10.3390/e20120969",
url="http://dx.doi.org/10.3390/e20120969"
}