The performance and optimization of ANN-WP model under unknown sea states

Ocean wave is one of the main factors to cause the 6 degrees of freedom motion of ships. Accurate wave surface prediction is paramount to ship safety. The artificial neural network-based wave prediction (ANN-WP) model is a phase-resolved wave prediction model based on artificial intelligence, which can provide high-precision prediction results of wave surface. However, it needs training with a large dataset to make the model obtain a satisfying prediction performance. When unknown sea states (not included in the training data) appear in practice, the reliability of the prediction results is low. In this study, the performance of the ANN-WP model under unknown sea states is investigated based on wave tank experiment. The data distribution of training data set is considered as one of the factors that affects the performance of ANN-WP model under unknown sea states. Based on this consideration, data mixing is employed to improve the performance of the ANN-WP model under unknown sea states. Through this method, the prediction error of the model for unknown sea states decreased by approximately 5.0%–10.0%. •The performance of ANN-WP model under unknown sea states is analyzed.•An optimized method based on mixing data is developed to improve the performance of ANN-WP model under unknown sea states.•Providing a way to improve the engineering application value of ANN-WP model.•Verification and comparison studies are carried out using experimental datasets.