A Literature Review: Enhancing Maritime Risk Assessment through Advanced Fuzzy Approach

Abstract Maritime transportation played a vital role in global trade and economic growth but faced risks from weather, human error, and equipment failures. Traditional risk assessment methods often fell short in addressing the complexities and uncertainties of maritime operations, highlighting the need for more effective approaches. This review examined the application of Fuzzy Bayesian Networks (FBN) in maritime risk assessment, focusing on its integration with fuzzy logic and probabilistic tools to improve safety. Findings indicated that combining fuzzy frequency and consequence analysis provided a more flexible and accurate way to assess risks, helping to predict and prevent accidents by deepening insights into the likelihood and impact of risk events. This integrated model facilitated tailored risk mitigation strategies, promoting safer and more resilient maritime operations. As the industry expanded, incorporating advanced fuzzy methods became essential for enhancing safety and decision-making. The evolving potential of FBN, particularly with big data and machine learning, underscored its role in fostering efficient, sustainable maritime practices. Future research was encouraged to refine these models and apply them to new technologies in risk management, aligning with the Sustainable Development Goals to enhance resilience and sustainability in the maritime sector.