Journal title
Entropy

   
Home  /  Entropy  /  Vol: 20 Núm: 12 Par: Decembe (2018)  /  Article
ARTICLE
TITLE

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

SUMMARY

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.

PAGES

 Articles related

Paulo Hubert, Linilson Padovese and Julio Michael Stern    

The problem of event detection in general noisy signals arises in many applications; usually, either a functional form of the event is available, or a previous annotated sample with instances of the event that can be used to train a classification algori... see more

Revista: Entropy

Lotfi Khribi, Brenda MacGibbon and Marc Fredette    

In the Bayesian framework, the usual choice of prior in the prediction of homogeneous Poisson processes with random effects is the gamma one. Here, we propose the use of higher order maximum entropy priors. Their advantage is illustrated in a simulation ... see more

Revista: Entropy

Yao Rong, Mengjiao Tang and Jie Zhou    

One main interest of information geometry is to study the properties of statistical models that do not depend on the coordinate systems or model parametrization; thus, it may serve as an analytic tool for intrinsic inference in statistics. In this paper,... see more

Revista: Entropy

Paul Darscheid, Anneli Guthke and Uwe Ehret    

When constructing discrete (binned) distributions from samples of a data set, applications exist where it is desirable to assure that all bins of the sample distribution have nonzero probability. For example, if the sample distribution is part of a predi... see more

Revista: Entropy

Daniel Ramos, Javier Franco-Pedroso, Alicia Lozano-Diez and Joaquin Gonzalez-Rodriguez    

In this work, we analyze the cross-entropy function, widely used in classifiers both as a performance measure and as an optimization objective. We contextualize cross-entropy in the light of Bayesian decision theory, the formal probabilistic framework fo... see more

Revista: Entropy