Student:	İshak ALTINPINAR
Supervisor:	Prof. Dr. Ersan BAŞAR
Department:	Deniz Ulaştırma İşletme Mühendisliği
Institution:	Graduate School of Natural and Applied Sciences
University:	Karadeniz Technical University Turkey

Title of the Thesis:	SAFETY CULTURE MODELING IN MARITIME
Level:	Ph.D.
Acceptance Date:	26/6/2020
Number of Pages:	110
Registration Number:	Di1379

Summary:

Safety culture is a main character in a series of industrial accident investigations that have

taken place since the accident take place in Chernobyl. It is widely accepted that organizations with

strong safety culture are effective in preventing work accidents. Understanding and modeling the

safety culture, where many variables are influential, can be complex and challenging. Techniques

that used for solving complex and challenging problems are improving day by day. For this reason,

artificial intelligence-based methods and statistical techniques were used together in this thesis.

While multiple logistic regression (MLR) was chosen among statistical techniques, artificial neural

networks (ANN) were preferred among artificial intelligence methods. The relation between a

dependent and many independent variables with more than two categorized can be examined by

MLR analysis. Meanwhile, ANN is a system which can imitate the human brain, learn like people in

this way, interpret the learned information, and can generate results using the outputs. In this way, it

seemed possible to use ANN and MLR in modeling the effect of safety culture which contains many

subtitles, on accidents. Within the context of this thesis, evaluation and classification of 218 accidents

and near misses based on subtitles of safety culture were performed with MLR analysis and ANN

method. MLR correctly classified 83.5% of the accident results and found six subtitles meaningful

for the model. ANN on the other hand, correctly classified 88.1% of the accident results and found

all of the subtitles meaningful for the model.

Keywords: Artificial Neural Networks, Maritime, Multiple Logistic Regression, Safety culture,

Seafarer