Real-time prediction of metacentric height of ro-ro passenger ships in Qiongzhou strait based on improved RBF neural network

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Addressing the complexities and real-time challenges in calculating ship metacentric height (GM), this study proposes an improved method using an optimized radial basis function neural network (RBFNN) for real-time GM prediction. Bayesian optimization is introduced to fine-tune the hyperparameters of the RBFNN, aiming to enhance the model's generalization performance. The study focuses on the Qiongzhou Strait Ro-ro passenger ship ‘Zijing No.11’ and selects three GM-related factors as neural network inputs using grey correlation analysis. The GM calculated by the empirical formula serves as the expected value, which is compared with predictions from various algorithms. Simulation results demonstrate that the improved RBFNN achieves significantly lower prediction errors compared to the unoptimized version. Furthermore, compared to other machine learning models and artificial neural networks, the proposed model exhibits superior performance in predicting ship initial stability height. Consequently, this model offers a practical tool for accurate and real-time GM prediction, enhancing intelligent stowage and operational efficiency in shipping.