Artificial intelligence seems to be a new point of view to classical problems that, in the past, could not be understood in depth, leaving certain gaps in each knowledge area. As an example of this, maritime accidents are one of the most recognised international problems, with clear environmental and human life consequences. From the beginning, statistical studies have shown that not only the typical sampled variables must be considered but the accidents are related to human factors that, at the same time, are related to some variables like fatigue that cannot be easily sampled. In this research work, the use of machine learning algorithms on over 300 maritime accidents is proposed to identify the relationship between human factors and the main variables. The results showed that compliance with the minimum crew members and ship length are the two most relevant variables related to each accident for the Spanish Search and Rescue (SAR) region, as well as the characteristics of the ships. These accidents could be understood as three main groups of accidents related to the general tendency to not meet the minimum number of crew members and its difference in the year of construction of the ship. Finally, it was possible to use neural networks to model accidents with sufficient accuracy (determination factor higher than 0.60), which is particularly interesting in the context of a control system for maritime transport.

The present paper shows an original study of more than 163 ship accidents in Spain showing which of the usually employed variables are related to each type of vessel accident due to the lack of information in this region. To this end, research was carried out based on the Spanish Commission for Investigation of Maritime Accidents and Incidents (CIAIM) reports. Detailed combinatory ANOVA analysis and Bayesian networks results showed a good agreement with studies of other regions but with some particularities per each type of accident analyzed. In particular, ship length was defined as the more relevant variable at the time to differentiate types of accidents. At the same time, both the year of build and the fact that the ship meets the minimum crew members required were excellent variables to model ship accidents. Despite this, the particularities of the Spanish Search and Rescue (SAR) region were defined at the time to identify accidents. In this sense, although variables like visibility and sea conditions were employed in different previous studies as variables related to accidents occurrences, they were the worst variables to define accidents for this region. Finally, different models to relate variables were obtained being the base of deterministic dynamic analysis. Furthermore, to improve the accuracy of the developed work some indications were obtained; revision of CIAIM accidents scales, identification of redundant variables, and the need for an agreement at the time to define the classification limits of each identification variable.

The transition towards low-carbon shipping has accelerated the adoption of alternative marine fuels such as ammonia, hydrogen, methanol and liquefied natural gas (LNG). Although these fuels reduce greenhouse gas emissions, they introduce new operational hazards related to toxicity, flammability, cryogenic storage and explosion risk. Existing comparative assessments are predominantly deterministic, representing accident probability and consequence severity by single values while neglecting operational uncertainty. This study proposes a probabilistic consequence-weighted risk assessment framework for alternative marine fuels operating within high-density Traffic Separation Schemes (TSSs). The methodology integrates historical traffic data, a location-specific contextual vulnerability multiplier and fuel-specific accident characteristics, propagating uncertainty through Monte Carlo simulation using Beta distributions for conditional loss-of-containment probability and triangular distributions for consequence severity. One million simulations per fuel-location combination were adopted after numerical convergence checks. The framework was applied to three Spanish TSSs (Tarifa, Cabo de Gata and Finisterre) and four candidate fuels (ammonia, hydrogen, methanol and LNG). Unlike deterministic approaches, the methodology provides complete risk distributions, P5–P95 uncertainty intervals and probabilistic fuel rankings. Results show that geographical location materially modifies operational risk and must be considered jointly with fuel-specific hazard; within the adopted scenario matrix, Tarifa remains the most critical location. Ammonia consistently exhibits the highest consequence-weighted risk. The framework is intended as a transparent comparative screening and decision-support tool rather than as a substitute for an absolute quantitative risk assessment based on physical consequence endpoints and observed accident frequencies.