arXiv:2605.30603v1 Announce Type: cross Abstract: Floating-material transport is influenced by unresolved processes that are often absent from available circulation products. We develop a data-driven transport-learning framework for learning effective transport corrections from limited Lagrangian observations using physically motivated ocean--atmosphere diagnostics and finite-memory representations motivated in part by inertial-particle memory effects. The diagnostic representation is analyzed through predictive and sparse symbolic-discovery approaches under leave-one-trajectory-out validation
The increasing availability of high-resolution satellite data and advances in AI and machine learning techniques enable the development of more sophisticated models for environmental phenomena like Sargassum transport.
Accurate prediction of Sargassum transport has significant implications for coastal economies, marine ecosystems, and maritime activities, particularly in regions burdened by massive Sargassum influxes.
The ability to learn effective transport dynamics from limited observations, integrated with existing circulation products, provides a more robust and adaptable framework for forecasting floating material movements.
- · Coastal tourism industries
- · Fishing industries
- · Maritime shipping
- · Environmental monitoring agencies
- · Regions unprepared for Sargassum influxes
- · Inflexible traditional oceanographic modeling approaches
Improved early warning systems for Sargassum blooms will reduce their negative economic and environmental impacts.
More effective resource allocation for Sargassum clean-up and management efforts, potentially leading to new marine resource industries.
Enhanced understanding of broader ocean current dynamics and climate change impacts on marine ecosystems, leading to better global environmental governance.
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