Predicting Ship Added Mass Coefficient with MACHINE LEARNING: Accuracy, Safety and Cost Savings

Abstract

Maritime accidents such as capsizing, storm-induced roll resonance, collisions and groundings continue to occur in Bangladesh’s inland and coastal waterways. While these events are usually linked to overloading, weather conditions, and maintenance issues, another important hydrodynamic factor - the Added Mass Coefficient (AMC) is rarely examined. Traditional methods for calculating AMC are too slow for use in real operations. In this study, we explored how different machine learning (ML) models, including Random Forest (RF), Neural Networks (NN), Support Vector Regression (SVR), Linear Regression (LR), and Long Short-Term Memory (LSTM) networks can predict AMC values from basic vessel parameters. Our results show that AMC can be considered not only as a design variable but also as an operational safety parameter. By predicting AMC in advance the models provide a way to support safety actions such as adjusting heading, controlling load distribution and reducing risks in shallow-water navigation. We also suggest that future work should combine real-time data with hybrid approaches to strengthen the reliability of predictions.