OceanLSTM: xLSTM with spatial attention for salt spray formation and migration prediction in marine hot-humid environments

Introduction Salt spray formation and migration in hot and humid marine environments have a significant impact on marine engineering and equipment maintenance. Accurately predicting these phenomena is crucial for reducing corrosion damage. Traditional research methodologies primarily utilize statistical models or physics-based simulations. Although these approaches yield satisfactory results within controlled conditions, they often encounter limitations in accurately capturing the complexity and variability inherent to marine environments. These methods struggle to capture the spatiotemporal dependencies of salt spray formation and migration. Moreover, they are typically difficult to apply in real-time and lack the ability to handle large-scale, dynamic data. Methods This study aims to address this issue by proposing the OceanLSTM model, which combines the temporal modeling capabilities of xLSTM with a spatial attention mechanism to capture the spatiotemporal relationships between complex environmental variables, thereby improving the accuracy of salt spray predictions. Results The experiments used several representative marine environment datasets, including the NOAA and Marine Aerosol datasets. The experimental results demonstrate that OceanLSTM significantly outperforms traditional models in evaluation metrics such as accuracy and F1-score, especially on datasets with strong spatiotemporal dependencies. Discussion This research provides a more precise and efficient tool for future marine environment monitoring and corrosion prediction, offering important practical applications.