Wave parameters in the nearshore are crucial for coastal engineering, shoreline protection, marine hazard assessment, and coastal management for climate resilience. Traditional monitoring systems like buoys and radar platforms offer accurate monitoring but can have high installation and maintenance expenses and limited spatial coverage. Passive ocean monitoring using video has been achieved by leveraging deep learning, however, many methods are not physically interpretable, feasible, and validated for oceanography. In thiswork, a Physics-Guided Deep Spatiotemporal Learning Framework for direct estimation of nearshore wave peak periods from passive coastal video stream is proposed. The framework combines automated temporal-variance based region-of-interest detection, multi-stage Sim-to-Real transfer learning, and physics-informed regularization to enhance the predictive accuracy and physical consistency. A variety of spatiotemporal architectures were assessed, such as transformer-based and recurrent-convolutional ones, alongside synthetic pretraining,silver-label adaptation, and expert fine-tuning. The results show that transformer-based architectures outperformed in terms of the accuracy of the instantaneous prediction, while lightweight recurrent-convolutional architectures achieved higher temporal stability and operational oceanographic skill. Ablation studies also demonstrated the benefits of physics-guided regularization in terms of trend-following consistency, and physically implausible predictions. Explainability auditing also helped to focus attention in hydrodynamically active surf-zone regions and showed good agreement with the physically derived wave propagation behavior. In general, the proposed framework shows the promise of physics-guided video-based deep learning systems for long-term coastal wave monitoring that are cost-efficient and operationally feasible.

翻译：近岸波浪参数对海岸工程、岸线防护、海洋灾害评估以及气候韧性海岸管理至关重要。传统监测系统（如浮标和雷达平台）能够提供精确监测，但安装和维护成本高昂，且空间覆盖范围有限。基于视频的被动海洋监测已通过深度学习技术实现，然而许多方法在海洋学层面缺乏物理可解释性、可行性和验证。本文提出了一种物理引导的深度时空学习框架，用于直接从被动式海岸视频流中估计近岸波浪波峰周期。该框架结合了基于时间方差的自动感兴趣区域检测、多阶段模拟到现实迁移学习以及物理信息正则化，以提升预测精度和物理一致性。我们评估了多种时空架构（如基于Transformer和循环卷积的架构），并辅以合成预训练、银标签自适应和专家微调。结果表明：基于Transformer的架构在瞬时预测精度上表现更优，而轻量级循环卷积架构在时间稳定性和海洋学业务技能方面更胜一筹。消融实验也证明了物理引导正则化在趋势跟随一致性和消除物理不可信预测方面的优势。可解释性审计有助于将注意力聚焦于水动力学活跃的碎波区，且与基于物理的波浪传播行为吻合良好。总体而言，该框架展示了物理引导的基于视频的深度学习系统在长期、低成本且业务可行的海岸波浪监测中的潜力。