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From conventional to machine learning methods for maritime riskassessment

Rawson A., Brito M., Sabeur Z., Tran-Thanh L.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2021

Vol. 15 No. 4

757--764

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.

Assessing the impacts to vessel traffic from offshore wind farms in the Thames Estuary

Rawson A., Rogers E.

Zeszyty Naukowe Akademii Morskiej w Szczecinie

2015

nr 43 (115)

99--107

The development of offshore renewable energy installations can introduce additional hazards to the safe navigation of shipping in often already crowded waterways. Developers and decision makers must predict and properly manage the potential risks imposed on navigating vessels from wind farm developments, in a complex and uncertain environment. Considerable analysis has been undertaken to model navigational risks to vessel traffic around wind farms; however this work is generally predictive and there is little understanding as to whether the modelling, central to the consideration of navigation safety, accurately reflects the postconstructed navigation risks. It is therefore important for decision makers to understand the uncertainties present in the analysis, both in terms of the assessment of risk and the implementation of any risk reduction measures. This paper presents a comparative analysis of the change in vessel traffic in the Thames Estuary before and after the construction of five offshore wind farms. The analysis demonstrates how the impact on vessel traffic is specific to the location of each development, driven by traffic management measures and other local constraints. Therefore the accurate modelling of this impact requires the input of experienced navigators, regulators and other knowledgeable stakeholders. The results of this analysis can be used to improve the predictive modelling of vessel traffic around offshore wind farms and other offshore installations, leading to a reduction in the uncertainty of vessel traffic modelling in the future.