Wave Height Forecasting in the Bay of Bengal Using Multivariate Hybrid Deep Learning Models

• Published in: Journal of Marine Science and Engineering
• Main Authors: Phyusin Thet, Aifeng Tao, Tao Lv, Jinhai Zheng
• Format: Article
• Language: English
• Published: MDPI AG 2025-07-01
• Subjects: CNN-LSTM; CNN-BiLSTM; CNN-GRU; CNN-BiGRU; univariate; multivariate
• Online Access: https://www.mdpi.com/2077-1312/13/8/1412

The development in coastal engineering and maritime transport demands accurate wave height prediction. In this study, hybrid deep learning models, including CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU, are employed to develop regional multivariate wave prediction models that incorporate multiple features, such as wave height, wind stress, water depth, pressure, and sea surface temperature (SST), for the entire Bay of Bengal area. Sensitivity analysis is performed to evaluate the accuracy using statistical metrics, such as the correlation coefficient, RMSE, and MAE. The findings demonstrate that regional multivariate models offer satisfactory results for the entire Bay of Bengal region. The multivariate model performs better compared to the univariate model as the forecast horizon increases. Performance assessment of each environmental factor, employing the integrated gradient method, reveals that sea surface temperature has the most significant influence, while wind stress is the least dominant factor in the wave prediction model. Among the tested models, the CNN-BiGRU has superior performance with a correlation of 0.9872, an RMSE of 0.1547, and an MAE of 0.1005 for the 3 h prediction and is proposed as the optimal model. This study contributes to assessing the contribution of each environmental feature and improving the accuracy of regional wave prediction.