DNN Predictive Model for Estimating the Metacetric Height of Small Fishing Vessels in South Korea at the Early Design Stages

Small fishing vessels are highly susceptible to stability-related accidents due to their limited size and vulnerability to rough seas. Although both international and Korean regulations mandate minimum stability standards, accurately estimating the metacentric height (G0M) during the early design stage—when detailed drawings or hydrostatic data are unavailable—remains a challenge. To address this gap, this study proposes a deep neural network (DNN)-based predictive model that estimates the G0M of small vessels using only fundamental hull dimensions and derived design variables, such as length-to-breadth ratio and length multiplied by block coefficient. A dataset comprising 118 Korean fishing vessels and 359 different loading conditions was constructed using parameters typically available in the early stages of ship design. These inputs do not require detailed hydrostatic calculations or structural drawings, making the approach practical for conceptual design. The model demonstrates strong predictive accuracy across diverse hull configurations and loading cases. Unlike conventional methods that depend on finalized designs or roll-period measurements, the proposed model enables quick and approximate stability assessments at the preliminary design phase. It serves as an efficient design support tool to allow naval architects to assess regulatory compliance and overall stability early in the development process, contributing to safer and more effective vessel design practices. In addition, by enabling fast and data-driven assessment of vessel stability, the proposed model may also serve as a foundational tool in broader maritime digitalization efforts, including intelligent ship design and ship-port logistics automation.