A Multimodal Deep Learning Framework for Ocean Wave Forecasting and Risk Assessment

Abstract

Accurate ocean wave prediction plays a critical role in maritime transportation, offshore engineering, renewable energy exploitation, and marine disaster prevention. Traditional numerical and statistical models often suffer from high computational costs and limited adaptability to complex ocean dynamics. This paper proposes a deep learning-based intelligent framework for ocean wave forecasting and marine risk assessment. By integrating spatiotemporal neural networks with multivariate oceanographic data, the proposed system enables accurate short-term and medium-term wave height prediction. Experimental results on real-world marine datasets demonstrate that the proposed approach significantly outperforms conventional methods in forecasting accuracy, robustness, and computational efficiency.