Research on ocean buoy attitude prediction model based on multi-dimensional feature fusion

Buoys, serving as crucial platforms for ocean observation, require precise predictions of their motion states, which are essential for buoy structure design, testing, and directly related to the stability and reliability of data collection. Leveraging data-driven methods instead of traditional software modeling analysis enables efficient analysis of the ocean environment’s impact on buoys. However, the coupling mechanisms between the ocean and the atmosphere complicate the pre-diction of buoy attitudes. In response to these challenges, this paper systematically analyzes the key ocean surface elements that affect buoy attitudes and innovatively applies the Pearson correlation coefficient to quantify the potential coupling relationships between these elements. The Recursive Feature Elimination with Cross-Validation (RFECV) algorithm is employed to select the optimal feature subset from a large number of raw features. Based on this, a Convolutional Neural Networks-Bidirectional Gated Recurrent Unit (CNN-BiGRU) buoy attitude prediction model is constructed. Experimental results demonstrate that the optimized prediction model, when combined with the feature selection algorithm, achieves a minimum prediction accuracy of 95.7%. This model not only reduces the dimensionality of the original data but also precisely captures the dynamics of ocean elements and their effects on buoy attitudes, leveraging the powerful feature extraction and fusion capabilities of CNN.