Sediment transport is a fundamental process shaping riverine and coastal systems, influencing infrastructure resilience, ecosystem health, and water resource sustainability. Although sediment transport is governed by well-established physical principles, collaboration between fundamental researchers and applied engineers and geomorphologists remains limited. AI offer transformative opportunities to advance sediment transport science, yet challenges remain in effectively integrating AI with process-based understandings. This session aims to bridge these gaps by bringing together experts in sediment transport, AI, and related disciplines to identify critical research needs and emerging opportunities for AI-enabled innovation.

 

We invite oral and poster presentations across three areas: (1) advancing predictive models of sediment dynamics using AI; (2) integrating remote sensing and real-time data assimilation with AI; and (3) developing hybrid approaches that couple physics-based and AI-driven models. The session will highlight how AI-driven approaches can improve sediment budgeting, enhance sediment routing and bypassing, and support adaptive infrastructure design and planning.

• H - Hydrology
• MR - Mineral and Rock Physics
• NH - Natural Hazards