Intelligent wave height prediction based on multitrunk fusion neural networks with bidirectional long short-term memory network

This article introduces a multi-backbone neural network fusion model that integrates a Bidirectional Long Short-Term Memory network with residual connections, while incorporating convolutional layers, fully connected layers, and attention mechanisms. The model uses real-world marine environment data as its primary input, analyzes the nonlinear relationships between different backbones through a neural network framework, and jointly predicts future changes in ocean wave height. By training the model, the predicted values demonstrate a minimal error range compared to the actual values, highlighting its strong predictive capabilities and accurate results. Furthermore, the fusion of multi-backbone neural network models offers a novel optimization approach for predicting future marine environments. This proposed model provides innovative solutions for forecasting simulated marine environment data and intelligently predicting ship motion responses.