Presentation Information
[BSG7SMHd-01]AI/ML-Augmented Metocean Digital Twins for Predictive and Actionable Maritime Intelligence
*Ruoying He1 (1. North Carolina State University (USA))
The maritime industry increasingly requires accurate, timely, and actionable insights to manage risks, optimize operations, and support sustainability. This presentation introduces an AI/MLaugmented Digital Twin of the Ocean (DTO) that integrates hyper-local, atmosphere–ocean–wave models with advanced data assimilation, cloud computing, and machine learning. By combining real-time observations with process-based modeling and AI-enhanced analytics, this framework creates a dynamic, interactive representation of the marine environment that can anticipate evolving conditions and support decision-making.
The talk will describe examples of how this innovation delivers predictive and actionable
intelligence for a wide range of maritime applications, including port operations, offshore
platforms, marine life protection, and search-and-rescue missions. Beyond operations, these
systems also enable scenario testing to evaluate climate impacts, guide spatial planning,
strengthen ecosystem resilience, and mitigate pollution risks.
Aligned with the UN Ocean Decade program DITTO and the IEEE P3501 standardization effort, this work emphasizes the importance of interoperability, engineering frameworks, and interface standards to ensure long-term scalability across science, industry, and policy. By advancing both the scientific foundations and practical implementations of DTOs, this research demonstrates how AI/ML-driven digital twins can transform metocean forecasting into predictive, decision-ready intelligence that supports a sustainable and resilient maritime future.
The talk will describe examples of how this innovation delivers predictive and actionable
intelligence for a wide range of maritime applications, including port operations, offshore
platforms, marine life protection, and search-and-rescue missions. Beyond operations, these
systems also enable scenario testing to evaluate climate impacts, guide spatial planning,
strengthen ecosystem resilience, and mitigate pollution risks.
Aligned with the UN Ocean Decade program DITTO and the IEEE P3501 standardization effort, this work emphasizes the importance of interoperability, engineering frameworks, and interface standards to ensure long-term scalability across science, industry, and policy. By advancing both the scientific foundations and practical implementations of DTOs, this research demonstrates how AI/ML-driven digital twins can transform metocean forecasting into predictive, decision-ready intelligence that supports a sustainable and resilient maritime future.
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