Study of A Hybrid Deep Learning Method for Forecasting the Short-Term Motion Responses of A Semi-Submersible

Accurately predicting motion responses is a crucial component of the design process for floating offshore structures. This study introduces a hybrid model that integrates a convolutional neural network (CNN), a bidirectional long short-term memory (BiLSTM) neural network, and an attention mechanism for forecasting the short-term motion responses of a semisubmersible. First, the motions are processed through the CNN for feature extraction. The extracted features are subsequently utilized by the BiLSTM network to forecast future motions. To enhance the predictive capability of the neural networks, an attention mechanism is integrated. In addition to the hybrid model, the BiLSTM is independently employed to forecast the motion responses of the semi-submersible, serving as benchmark results for comparison. Furthermore, both the 1D and 2D convolutions are conducted to check the influence of the convolutional dimensionality on the predicted results. The results demonstrate that the hybrid 1D CNN-BiLSTM network with an attention mechanism outperforms all other models in accurately predicting motion responses.