Data-Driven Prediction of Anchor Dragging of Vessels under Typhoon Conditions

The increasing intensity of typhoons associated with climate change has significantly elevated the risk of anchor dragging of vessels sheltering in coastal anchorages, leading to collisions, groundings, and secondary maritime accidents. Despite its operational importance, anchor dragging under extreme weather conditions remains poorly understood and is rarely addressed as a predictive problem. This study proposes a data-driven framework to detect and predict anchor dragging of anchored vessels during typhoon events by integrating vessel motion data with meteorological and oceanographic forcing. Automatic Identification System (AIS) data were combined with typhoon track and intensity information, high-resolution marine weather fields, and bathymetric data for Typhoon Kong-Rey (2018), which directly affected Jinhae Bay, one of the largest typhoon shelter areas in Korea. Anchored vessels were identified using speed-based criteria, and vessel-specific anchor circles were constructed by estimating anchor positions from AIS heading information, anchor chain length, and vessel dimensions. Anchor dragging events were labeled based on deviations from the anchor circle, supported by visual verification. To predict dragging occurrence, a genetic algorithm-based automated machine learning framework (TPOT) was applied to optimize preprocessing steps, feature selection, model structure, and hyperparameters. The explanatory variables included vessel kinematics, wind speed and direction, atmospheric pressure, and local water depth. The resulting model successfully distinguished high-risk vessels during peak typhoon influence, demonstrating strong predictive performance and robustness across vessel types. The proposed approach provides a probabilistic early-warning capability for anchor dragging, enabling prioritized monitoring of high-risk vessels rather than uniform risk management. This framework supports proactive decision-making for Vessel Traffic Services (VTS), port authorities, and emergency response agencies, and contributes to reducing cascading maritime accidents under intensifying extreme weather conditions.