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AI Research
arXiv CS.AI·Jun 12·5 min read
Physics-Guided Spatiotemporal Learning for Coastal Wave Peak Period Estimation from Video
TL;DR
Coastal wave monitoring is essential for various applications but often relies on expensive and limited traditional systems. A Physics-Guided Deep Spatiotemporal Learning Framework was developed to estimate wave peak periods from passive video streams.
✦ Why It Matters
Engineers can leverage this framework for efficient and accurate coastal wave monitoring using existing video technology.
Key Takeaways
- Explore physics-guided methods for improved model accuracy.
- Utilize video streams for cost-effective coastal monitoring.
- Implement explainability auditing to enhance model transparency.
- Consider lightweight architectures for better operational stability.
Full Summary
Monitoring wave parameters in coastal areas is vital for engineering and climate resilience, yet traditional methods like buoys are costly and spatially limited. A new approach, the Physics-Guided Deep Spatiotemporal Learning Framework, was created to estimate nearshore wave peak periods using passive video streams.
This framework integrates automated region-of-interest detection, multi-stage Sim-to-Real transfer learning, and physics-informed regularization to enhance both accuracy and physical relevance. Various architectures were tested, revealing that transformer-based models excelled in instantaneous prediction accuracy, while lightweight recurrent-convolutional models provided better temporal stability.
The use of physics-guided regularization improved trend-following consistency and reduced physically implausible predictions. Explainability auditing highlighted hydrodynamically active surf-zone areas, aligning well with established wave propagation behaviors.
Overall, this framework offers a cost-effective and operationally feasible solution for long-term coastal wave monitoring.