Leading indicators and maritime safety: predicting future risk with a machine learning approach

Abstract

Abstract The shipping industry has been quite successful in reducing the number of major accidents in the past. In order to continue this development in the future, innovative leading risk indicators can make a significant contribution. If designed properly, they enable a forward-looking identification and assessment of existing risks for ship and crew, which in turn allows the implementation of mitigating measures before adverse events occur. Right now, the opportunity for developing such leading risk indicators is positively influenced by the ongoing digital transformation in the maritime industry. With an increasing amount of data from ship operation becoming available, these can be exploited in innovative risk management solutions. By combining the idea of leading risk indicators with data and algorithm-based risk management methods, this paper firstly establishes a development framework for designing maritime risk models based on safety-related data collected onboard. Secondly, the development framework is applied in a proof of concept where an innovative machine learning-based approach is used to calculate a leading maritime risk indicator. Overall, findings confirm that a data- and algorithm-based approach can be used to determine a leading risk indicator per ship, even though the achieved model performance is not yet regarded as satisfactory and further research is planned.

Keywords

• Maritime safety
• Accident prevention
• Safety management
• Risk prediction
• Leading indicators
• Machine learning