Significant wave height prediction based on the GVSAO-CNN-BiGRU-SA model

To improve the accuracy and robustness of significant wave height prediction under complex marine conditions, a multi-strategy Snow Ablation Optimization (GVSAO) model based on the Good Point Set Initialization Strategy (G), Cyclic Oscillation Mutation Strategy (V), and Snow Ablation Optimizer (SAO) is proposed to enhance parameter optimization. The GVSAO model combines Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Units (BiGRU), and Self-Attention Mechanism (SA) to construct the GVSAO-CNN-BiGRU-SA framework, which fully exploits the nonlinear characteristics of wave height time series. The study utilizes observed data from two observation points along the U.S. East Coast to the Gulf of Mexico (Stations 41013 and 42002) as well as from the Arabian Sea (Station 23020) and the Pacific Ocean (Station 46044). Input feature variables were selected through correlation analysis, and Variational Mode Decomposition (VMD) was employed to decompose wave height signals and extract autocorrelation features. The results demonstrate that the GVSAO model outperforms SAO, GSAO, and VSAO in terms of adaptability and stability, as validated by performance comparisons on the CEC2005 benchmark functions (F7, F9, F10, and F11). Autocorrelated variables derived from VMD significantly improved prediction accuracy by reducing input redundancy. Compared with the BiGRU model, the GVSAO-CNN-BiGRU-SA model exhibited superior performance, with RMSE reduced by 44.01% at Station 41013 and 15.12% at Station 42002. Similarly, it outperformed the CNN-BiGRU and CNN-BiGRU-SA models across all key metrics. The GVSAO-CNN-BiGRU-SA model also achieved high-accuracy predictions in diverse marine environments, including the Arabian Sea (Station 23020) and the Pacific Ocean (Station 46044), with relative mean errors within 0.5472%, RMSE within 0.1064 m, and correlation coefficients exceeding 99.33%. The GVSAO-CNN-BiGRU-SA model provides a reliable solution for wave height prediction, contributing to marine engineering and energy utilization under complex marine conditions.