Dynamic Biofouling Risk Assessment for Ships Using AIS Data and Environmental Factors

Abstract

Marine biofouling on ship hulls poses a dual challenge to the global shipping industry, leading to significant penalties in hydrodynamic performance and increasing the risk of invasive aquatic species (IAS) transfer. Traditional biofouling management relies heavily on fixed-interval maintenance or reactive cleaning based on visible speed loss, which lacks precision and often fails to address the dynamic nature of biological growth. This study proposes a novel, data-driven framework for assessing biofouling risk by integrating high-frequency Automatic Identification System (AIS) kinematic data with spatiotemporal environmental exposure parameters. The model quantifies risk through a multi-factor index that accounts for vessel downtime, sea surface temperature, and traversal of ecologically sensitive geographic zones. A case study involving a coastal bulk carrier and a global ocean-going vessel was conducted to validate the framework. Results demonstrate that the model can effectively distinguish between static-driven fouling risks in coastal operations (dominated by 94% slow-flow events) and metabolic-driven risks in global operations (dominated by 44% high-temperature exposure). The system achieved a classification accuracy of approximately 87% against a validation dataset, with a 100% detection rate for entries into known high-risk estuarine areas. By transforming abstract biological pressures into a quantifiable engineering metric, this research provides ship operators with a proactive decision-support tool to optimize hull cleaning schedules, thereby enhancing fuel efficiency and ensuring compliance with emerging environmental regulations such as the IMO’s Carbon Intensity Indicator (CII).