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Physics-Guided Spatiotemporal Learning for Coastal... | AI Research

Key Takeaways

Physics-Guided Spatiotemporal Learning for Coastal Wave Peak Period Estimation from Video This research introduces a new deep learning framework designed to...
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.

Paper AbstractExpand

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.

Physics-Guided Spatiotemporal Learning for Coastal Wave Peak Period Estimation from Video
This research introduces a new deep learning framework designed to estimate the peak period of ocean waves—a critical metric for coastal management and climate resilience—directly from standard coastal video footage. While traditional monitoring methods like buoys are accurate, they are expensive and have limited coverage. This study provides a cost-effective, automated alternative that uses video analysis to track wave behavior without requiring complex, manual preprocessing steps like constructing time-stack images or performing spectral analysis.

How the Approach Works

The framework uses a three-stage transfer learning process to bridge the gap between raw video data and accurate physical measurements. First, the system is pre-trained on synthetic video sequences generated using Airy wave theory, which teaches the model the fundamental physics of how waves propagate. Second, it is trained on a large "silver" dataset of real-world coastal videos with automatically generated labels. Finally, the model is fine-tuned using a "golden" dataset of high-quality, expert-annotated videos to ensure it aligns with real-world conditions.
To improve accuracy, the framework includes an automated region-of-interest (ROI) detector. By analyzing the temporal variance of pixels, the system identifies the active surf zone and ignores static elements like land or sky. Additionally, the model incorporates a physics-guided loss function that penalizes predictions falling outside the physically plausible range of 8 to 20 seconds, ensuring the results remain consistent with oceanographic reality.

Key Findings and Performance

The study compared several architectures, including transformer-based models and recurrent-convolutional networks. The results indicated that transformer-based architectures were superior for achieving high accuracy in instantaneous predictions. In contrast, lightweight recurrent-convolutional models were better suited for operational use, offering higher temporal stability.
Ablation studies confirmed that the physics-guided regularization was essential for maintaining trend-following consistency and preventing physically impossible predictions. Furthermore, explainability audits showed that the model successfully focused its attention on hydrodynamically active regions, aligning closely with how waves actually behave in nature.

Important Considerations

While the framework shows great promise for long-term, cost-efficient coastal monitoring, there are limitations to note. The current "golden" dataset of expert-annotated videos is relatively small, which limited the researchers' ability to perform a fully independent held-out test partition in this study. Consequently, the reported performance metrics reflect the model's behavior on the data used during the fine-tuning phase. Future work aims to expand this dataset to allow for more rigorous, independent testing across a wider variety of coastal environments.

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