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Descripción
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| 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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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Entropy |
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ISSN
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1099-4300 |
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Factor de impacto JCR
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1,821 |
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Información de impacto
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Datos JCR del año 2016 |
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Volumen
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20 |
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DOI
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10.3390/e20120969 |
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Número de revista
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Desde la página
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969 |
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Hasta la página
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988 |
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Mes
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SIN MES |
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Ranking
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