Big Data Analytics Methods for Collision and Grounding Risk Analysis in Real Conditions: Framework, Evaluation, and Applications

Description

Collisions and groundings are the most frequent maritime accidents. They often lead to devastating consequences. Collision and grounding risks can be evaluated qualitatively by expert judgment or quantitatively through the analysis of maritime traffic data. Yet, data-driven studies reflecting real operational conditions using big data remain limited. A big data analytics framework will be introduced to support proactive risk management practice, intelligent monitoring, and the implementation of associated risk control options in accordance with existing and emerging regulatory requirements. The framework directed by machine learning methods, can assist with (1) the identification of critical collision and grounding scenarios that are not currently accounted for by existing accident databases,(2) the idealization of the fleet at risk in real conditions, (3) the definition of novel collision and grounding risk criteria for ad hoc use within the context of emerging performance-based standards, and (4) the prediction of time-varying ship motion trajectories for recognizing risky situations in advance and proactive risk mitigation.
The methods presented in this thesis are validated in the Gulf of Finland. Results indicate that a big data analytics framework could help develop maritime risk management tools and intelligent decision support systems for ships in operation

Period	18 Jan 2023
Examinee	Mingyang Zhang
Examination held at	Aalto University
Degree of Recognition	International