IVYA-FMGRU: A frequency-domain context interaction model with bio-inspired optimization for significant wave height prediction

Accurate prediction of significant wave heights is crucial for the safety of marine structures and ships. Traditional models struggle to capture the key frequency and periodic characteristics in wave height data. To address this issue, a novel Ivy Algorithm-Fast Fourier Transform Mogrifier Gated Recurrent Unit (IVYA-FMGRU) model is proposed, which integrates the gated recurrent unit (GRU) with the fast Fourier transform (FFT) and Mogrifier operations. The FFT extracts periodic features, the Mogrifier enhances the interaction between the GRU and frequency information, and the Ivy algorithm (IVYA), a bio-inspired optimization method, optimizes the model parameters. In addition, random forest (RF) is employed for feature selection. Experimental results show that the IVYA-FMGRU model achieves R² scores of 0.8505, 0.8683, and 0.8910 on datasets 46027, 46083, and 46084, respectively outperforming other baseline models. Furthermore, error statistical analysis across different wave height intervals confirms the model’s accuracy and stability within each interval, demonstrating its superior performance and generalization capability in wave height prediction.