The maritime industry, responsible for moving approximately 90% of the world's goods,
significantly contributes to environmental pollution, accounting for around 2.5% of global …

This review article explores the applications and impacts of Machine Learning (ML)
techniques in marine traffic management and prediction within complex maritime systems. It …

The global shipping industry faces increasingly complex safety challenges due to the rapid
growth of international maritime trade. This study develops a novel framework that combines …

Maritime transportation plays a crucial role in global trade and economic development.
However, this industry is exposed to various risks (eg, natural disasters), which can cause …

Early warning on the basis of RIFs data is widely considered as an effective way to prevent
waterborne transportation accidents, and the performance of warning model is critical. To …

Sea-level rise in Benin coastal zones leads to risks of erosion and flooding, which have
significant consequences on the socio-economic life of the local population. In this paper …

Predicting the risk of maritime accidents is crucial for improving traffic surveillance and
marine safety. The availability of data sources and development of machine learning and …

Abstract Machine learning (ML), particularly, Automated machine learning (AutoML) offers a
range of possibilities for analysing large volumes of historical maritime accidents data with …

Two recent trends made this project possible:(1) The recognition that near misses can be
predictors of future negative events and (2) enhanced artificial intelligence (AI) and machine …

Maritime accidents, such as ship collisions and oil spills, directly affect maritime
transportation, pollute the water environment, and indirectly threaten life and property safety …