Integrating AI, physics, and real-world data offers systemic insights into coastal transformation under climate change
Original framing: “Forecasting coasts may improve by combining AI, physics, and real-world data” — Phys.org
Historically, coastal regions have been shaped by both natural processes and human interventions, such as the construction of levees and the draining of wetlands. These interventions have often led to unintended consequences, such as increased erosion and flooding, which are now being exacerbated by climate change.
The integration of AI, physics, and real-world data into coastal forecasting represents a significant step forward in understanding climate-driven change.
However, this approach must be embedded within a broader systemic framework that acknowledges historical land-use decisions, Indigenous knowledge, and the disproportionate impacts on marginalized communities. By combining scientific modeling with participatory governance and cross-cultural insights, we can move toward more equitable and effective coastal management. Lessons from the Mekong Delta and the Sundarbans demonstrate that hybrid approaches—blending traditional and modern knowledge—can enhance resilience and inform policy. Ultimately, the success of these models depends on their ability to reflect the lived realities of those most vulnerable to coastal transformation.