Skip to main content

From Conventional to Machine Learning Methods for Maritime Risk Assessment.

Rawson, A., Brito, M., Sabeur, Z. and Tran-Thanh, L., 2021. From Conventional to Machine Learning Methods for Maritime Risk Assessment. Transnav-International journal on marine navigation and safety of sea transportation, 15 (4), 757-764.

Full text available as:

From Conventional to Machine Learning Methods for Maritime Risk Assessment.pdf - Published Version
Available under License Creative Commons Attribution.


DOI: 10.12716/1001.15.04.06


Within the last thirty years, the range and complexity of methodologies proposed to assess maritime risk have increased significantly. Techniques such as expert judgement, incident analysis, geometric models, domain analysis and Bayesian Networks amongst many others have become dominant within both the literature and industry. On top of this, advances in machine learning algorithms and big data have opened opportunities for new methods which might overcome some limitations of conventional approaches. Yet, determining the suitability or validity of one technique over another is challenging as it requires a systematic multicriteria approach to compare the inputs, assumptions, methodologies and results of each method. Within this paper, such an approach is proposed and tested within an isolated waterway in order to justify the proposed advantages of a machine learning approach to maritime risk assessment and should serve as inspiration for future work.

Item Type:Article
Additional Information:License Terms and Copyright The journal operates under the Creative Commons Attribution 3.0 licence. Authors retain the copyright and publishing rights of all articles and materials published in the journal under their name as long as proper attribution is respected.
Uncontrolled Keywords:Risk Assessment, Maritime Risk Assessment, Machine Learning Method, Bayesian Networks, Machine Learning, Machine Learning Algorithms, Multicriteria Approach, Maritime Risk
Group:Faculty of Science & Technology
ID Code:36771
Deposited By: Symplectic RT2
Deposited On:22 Mar 2022 14:21
Last Modified:22 Mar 2022 14:21


Downloads per month over past year

More statistics for this item...
Repository Staff Only -