Risk assessment of flammable liquid transportation on waterways: An ontology-driven dynamic Bayesian network approach

Abstract

Accidents involving the transportation of flammable liquids on waterways often lead to severe consequences, highlighting the importance of effective risk assessment under conditions of uncertainty. This paper presents a novel methodology for evaluating the risk associated with the spatiotemporal evolution of flammable liquid transportation on waterways. By leveraging ontology models and dynamic Bayesian networks, the approach involves analyzing factors that impact risk, constructing a standardized knowledge representation model, and mapping this onto a dynamic Bayesian network for comprehensive risk assessment. The methodology incorporates fuzzy theory and the Best-Worst Method to calculate probabilities within the Bayesian framework, enabling detailed analysis of factor impacts on transportation risk. A practical application is illustrated through the development of a dynamic risk evaluation model for octane transportation in the Yangtze River, demonstrating the model's capability to predict risk under varying conditions. This ontology-driven Bayesian network model provides a robust foundation for making informed management decisions in the transportation of flammable liquids, effectively addressing the challenges of semantic expression and inferencing under uncertainty to enhance safety in waterway transportation.