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Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors

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,...

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Detalles Bibliográficos
Autores principales: Arnaldo Valdés, Rosa María, Liang Cheng, Schon Z.Y., Gómez Comendador, Victor Fernando, Sáez Nieto, Francisco Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512568/
https://www.ncbi.nlm.nih.gov/pubmed/33266693
http://dx.doi.org/10.3390/e20120969
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author Arnaldo Valdés, Rosa María
Liang Cheng, Schon Z.Y.
Gómez Comendador, Victor Fernando
Sáez Nieto, Francisco Javier
author_facet Arnaldo Valdés, Rosa María
Liang Cheng, Schon Z.Y.
Gómez Comendador, Victor Fernando
Sáez Nieto, Francisco Javier
author_sort Arnaldo Valdés, Rosa María
collection PubMed
description 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.
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spelling pubmed-75125682020-11-09 Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors Arnaldo Valdés, Rosa María Liang Cheng, Schon Z.Y. Gómez Comendador, Victor Fernando Sáez Nieto, Francisco Javier Entropy (Basel) Article 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. MDPI 2018-12-14 /pmc/articles/PMC7512568/ /pubmed/33266693 http://dx.doi.org/10.3390/e20120969 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Arnaldo Valdés, Rosa María
Liang Cheng, Schon Z.Y.
Gómez Comendador, Victor Fernando
Sáez Nieto, Francisco Javier
Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors
title Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors
title_full Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors
title_fullStr Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors
title_full_unstemmed Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors
title_short Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors
title_sort application of bayesian networks and information theory to estimate the occurrence of mid-air collisions based on accident precursors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512568/
https://www.ncbi.nlm.nih.gov/pubmed/33266693
http://dx.doi.org/10.3390/e20120969
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