Influences of variables on ship collision probability in a Bayesian belief network model

Maria Hänninen, Pentti Kujala

Research output: Contribution to journalArticleScientificpeer-review

114 Citations (Scopus)

Abstract

The influences of the variables in a Bayesian belief network model for estimating the role of human factors on ship collision probability in the Gulf of Finland are studied for discovering the variables with the largest influences and for examining the validity of the network. The change in the so-called causation probability is examined while observing each state of the network variables and by utilizing sensitivity and mutual information analyses. Changing course in an encounter situation is the most influential variable in the model, followed by variables such as the Officer of the Watchs action, situation assessment, danger detection, personal condition and incapacitation. The least influential variables are the other distractions on bridge, the bridge view, maintenance routines and the officers fatigue. In general, the methods are found to agree on the order of the model variables although some disagreements arise due to slightly dissimilar approaches to the concept of variable influence. The relative values and the ranking of variables based on the values are discovered to be more valuable than the actual numerical values themselves. Although the most influential variables seem to be plausible, there are some discrepancies between the indicated influences in the model and literature. Thus, improvements are suggested to the network.

Original languageEnglish
Pages (from-to)27-40
Number of pages14
JournalReliability Engineering and System Safety
Volume102
DOIs
Publication statusPublished - Jun 2012
MoE publication typeA1 Journal article-refereed

Keywords

  • Bayesian networks
  • Causation probability
  • Maritime accidents
  • Mutual information
  • Sensitivity analysis

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