Collaborative research: caig: inferring hidden urban flood controls using physics-informed multimodal learning

The National Science Foundation Office of Integrative Activities awarded The Research Foundation For The State University of New York, operating as Stony Brook University, $537,060 on October 1, 2026, under the Geosciences program (CFDA 47.050) to develop a physics-informed multimodal artificial intelligence framework for inferring hidden urban flood controls from unstructured evidence such as reports, photographs, and videos. The recipient will establish flood datasets and physics-based simulation benchmarks focused on greater New York; advance geospatial-aware language and vision technologies to estimate flood depth from unstructured evidence with quantified uncertainty; design inversion-enabled surrogate flood models that use sparse depth constraints to infer latent drainage and surface-flow controls; and analyze how inferred controls vary across storms, urban forms, and cities. The work supports flood-risk assessment, infrastructure planning, student training, and public understanding of urban flooding and artificial intelligence through shared data, software, and educational outreach. Performance occurs in Stony Brook, New York, with a period of performance from October 1, 2026, through September 30, 2029. No sub-awards are planned.

Abstract

Urban flooding increasingly threatens infrastructure and communities across the united states, where extreme rainfall interacts with dense built environments and aging stormwater systems. A major challenge is that cities often lack flood-depth information during storms, while many important conditions that control flood response are hidden or rarely measured. This project will investigate how reports, photographs, videos, and other unstructured evidence of flooding can help reveal hidden urban flood controls. The project will support flood-risk assessment and infrastructure planning, while training students, engaging city agencies, and broadening public understanding of urban flooding and artificial intelligence through shared data, software, and educational outreach. This project will develop a physics-informed multimodal artificial intelligence framework for inferring and explaining hidden system controls in urban flooding. Focusing on greater new york, the project will establish flood datasets and physics-based simulation benchmarks; advance geospatial-aware language and vision technologies to estimate flood depth from unstructured evidence with quantified uncertainty; design inversion-enabled surrogate flood models that use sparse depth constraints to infer latent drainage and surface-flow controls; and analyze how inferred controls vary across storms, urban forms, and cities. Together, these efforts will establish a new modeling paradigm for inferring hidden urban flood controls from indirect flood observations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are not planned for this award.