Causation analysis of container ship collision accidents based on improved BN

doi:10.16265/j.cnki.issn1003-3033.2019.10.006

Abstract

Abstract: In order to address BN structure learning difficulties caused by insufficient data samples when BN is used to analyze causation of container ship collisions, a BN structure learning algorithm based on kernel density estimation and model weighted averaging was proposed. Firstly, kernel density estimation was used to expand small data set so as to meet minimum data size of BN structure learning. Then, model averaging strategy was utilized to integrate various learning algorithms by allocating different weights, which improved learning effect of the algorithm with small sample data. Finally, a BN model was established to analyze causation of container ship collision accidents based on a small number of samples. The results show that this proposed algorithm can quantitatively analyze causation of collision accidents on the basis of small sample data, and obtain accident causation chain. It is helpful to improve safety of container shipping.

Key words: small sample data, Bayesian network (BN), kernel density estimation, model averaging, causation analysis

CLC Number:

X928.03

SI Dongsen, ZHANG Yingjun, LANG Kun. Causation analysis of container ship collision accidents based on improved BN[J]. China Safety Science Journal, 2019, 29(10): 31-37.

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