Hybrid Intelligence for Wave Prediction: Integrating Sparse LSTM-Transformer with an Expert System

Abstract Accurate and computationally efficient wave height prediction is critical for marine operations, coastal engineering, and offshore safety. However, existing models struggle to capture both short- and long-term dependencies in complex sea states. This study presents a hybrid forecasting framework that combines Long Short-Term Memory (LSTM) networks and Transformer encoders, augmented by a sparse attention mechanism to reduce computational cost while preserving key temporal features. To further enhance reliability under extreme conditions, a knowledge-based expert system is incorporated, consisting of 21 domain-specific rules addressing physical constraints, safety thresholds, and control logic. These rules refine the model’s outputs through a confidence-weighted fusion approach. The model is evaluated using five years of half-hourly wave height data from the Muluraba buoy station. Experimental results demonstrate that the proposed hybrid system outperforms traditional statistical models (e.g., ARIMA) and baseline deep learning models (e.g., CNN-LSTM, standard LSTM-Transformer), achieving a correlation coefficient of 0.97, RMSE of 0.027, and MAE of 0.019. These results demonstrate the effectiveness of integrating neural networks with expert systems for robust wave forecasting.