Ersan Başar
Karadeniz Technical University
33 Papers
45 Citations
Ersan Başar is an academic researcher from Karadeniz Technical University. The author has contributed to research in topics: Human error & Turkish. The author has an hindex of 7, co-authored 31 publications.
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Papers
Assessment of collisions and grounding accidents with human factors analysis and classification system (HFACS) and statistical methods
TL;DR: The most important causes are identified as human factor differences between collision and grounding accidents, decision errors, resource management deficiencies, violations, skill-based errors and miscommunication.
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Analysis of grounding accidents caused by human error
TL;DR: In this article, the authors examined the maritime accident reports issued for grounded ships between 1993 and 2011 and found that the most significant causes of these types of accidents are, lack of communication and coordination in Bridge Resource Management, position-fixing application errors, lookout errors, interpretation errors, use of improper charts, inefficient use of bridge navigation equipment, and fatigue.
The analysis of ship accident occurred in Turkish search and rescue area by using decision tree
Sercan Erol,Ersan Başar +1 more
TL;DR: In this article, the authors investigated the cause of 1247 marine accidents occurring in Turkish search and rescue area were investigated in 2001-2009 and analyzed by using the Decision Tree method.
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The analysis of life safety and economic loss in marine accidents occurring in the Turkish Straits
TL;DR: In this article, the authors focused on marine accidents in the Turkish Straits that have done serious harm to humans, the natural environment, and the economy, and proposed measures to ensure that ship personnel are compete.
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Multiple linear regression analysis and artificial neural networks based decision support system for energy efficiency in shipping
Orkun Burak Öztürk,Ersan Başar +1 more
TL;DR: In this paper, decision support systems (DSS) have been established with the fuel oil consumption (FOC) prediction methods of Multiple Linear Regression Analysis (MLRA) and Artificial Neural Networks (ANN) to reduce air pollution from ships and operational costs in shipping by implementing efficiency measures of voyage management.
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