Earth Surface Dynamics
Earth Surface Dynamics
ESurf
Articles | Volume 14, issue 2
Articles | Volume 14, issue 2
https://doi.org/10.5194/esurf-14-313-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-14-313-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 2
Research article
 | 
22 Apr 2026
Research article |  | 22 Apr 2026

An integrated deep learning framework enables rapid spatiotemporal morphodynamic predictions toward long-term simulations

Mohamed M. Fathi, Zihan Liu, Anjali M. Fernandes, Michael T. Hren, Dennis O. Terry Jr., C. Nataraj, and Virginia Smith

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Short summary
Understanding and predicting the evolution of river landscapes is critical for effective river management. Traditional physics-based morphodynamic models, while accurate, are computationally intensive and often impractical for long-term applications. This study presents a robust deep learning framework, which was designed to overcome the computational limitations by enabling rapid and reliable predictions of hydrodynamic and sediment transport behaviors.
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