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New paper out in Journal of Hydrology

Multi-resolution reconstruction of longitudinal streambed footprints using embedded sparse convolutional autoencoders

https://www.sciencedirect.com/science/article/pii/S0022169425001908?via%3Dihub

<aside> <img src="/icons/duck_yellow.svg" alt="/icons/duck_yellow.svg" width="40px" /> Keypoints:

✅ AI-driven multi-resolution reconstruction

✅ Reliable under sparse and corrupted data

✅ A step towards digital twinning of river systems

We introduce a convolutional autoencoder framework to reconstruct high-resolution streambed footprints from sparse data. Our model leverages deep learning, transfer learning, and attention mechanisms to recover fine-scale riverbed patterns from sparse observations—even under severe data loss.

</aside>

New paper out in npj Natural Hazards

Digital twinning of river basins towards full-scale, sustainable and equitable water management and disaster mitigation

https://www.nature.com/articles/s44304-024-00047-2

<aside> <img src="/icons/duck_yellow.svg" alt="/icons/duck_yellow.svg" width="40px" /> Keypoints:

• Innovative approaches for understanding and managing complex river basin dynamics, with implications for sustainable water management and disaster mitigation.
• We examine cutting-edge methodologies and offer new insights into digital twinning, full-scale modeling of water-related processes
• Development of tools that support equitable, long-term solutions for water resource management. </aside>