做海岸工程或海洋监测的团队，终于有了一个成本低、可解释的AI方案——从视频直接估算波浪参数，比布设浮标省钱省力，值得关注。
该研究提出一种物理引导的深度时空学习框架，用于从被动海岸视频流直接估算近岸波浪峰值周期。框架结合了基于时间方差的感兴趣区域检测、多阶段模拟到真实迁移学习以及物理信息正则化，提升了预测精度和物理一致性。实验表明，基于Transformer的架构在瞬时预测精度上表现最佳，而轻量级循环卷积架构在时间稳定性和海洋学技能上更优。消融研究证实了物理引导正则化在趋势一致性方面的优势，可解释性审计显示模型关注了水动力活跃的破浪区。该工作展示了基于视频的深度学习系统在长期、低成本海岸波浪监测中的潜力。
Physics-Guided Spatiotemporal Learning for Coastal Wave Peak Period Estimation from Video
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.