This project explores a novel approach to flood forecasting by integrating physics-informed artificial intelligence (AI) with satellite embedding datasets.
Unlike traditional hydrodynamic simulations that demand high-performance computing, this research validates AlphaEarth’s cutting-edge satellite embeddings to capture flood susceptibility at a continental scale using standard infrastructure. The innovation lies in embedding physical conservation laws such as mass, momentum, and energy directly into neural architectures, ensuring predictions are not only computationally efficient but also physically interpretable. This breakthrough addresses the long-standing trade-off between accuracy, scalability, and interpretability in environmental modeling, positioning AI as a trusted tool for climate adaptation.
The project’s impact extends beyond technical advancement to real-world resilience. By deploying physics-informed models through the Floodwatch.io platform, the research delivers high-resolution flood-risk maps and scalable early warning systems accessible to vulnerable communities lacking advanced infrastructure. Ultimately, this work establishes a precedent for embedding physical laws into AI-driven environmental monitoring, enabling equitable disaster preparedness and accelerating adoption across disciplines such as drought, wildfire, and extreme heat prediction.
