Seed Grant Projects | Funded Teams
Coastal flooding prediction and mitigation: Integrating high-fidelity computer models with field observations


The rapid growth of coastal cities and communities is increasing the vulnerability of people and infrastructure to flood hazard. Developing social and civil infrastructure that is resilient to flooding, storm surge, and accelerating sea-level rise remains a monumental challenge that requires us to leverage large datasets and state-of-the-art computer simulations, but we currently lack an integrated framework for the prediction and assessment of coastal flood hazard that takes advantage of modern computational resources. Here, we propose to develop a unified and standardized flood hazard assessment system that integrates high-fidelity computer models with field observations. We will focus on two urban mega-regions, Boston and New York, where we will (i) develop state-of-the-art storm surge, wind wave and sediment transport models and validate against field observations, and (ii) apply data mining and deep learning technology to both field and simulation data to improve our understanding of coastal flood hazard and test adaptation strategies. The proposed research develops an innovative and interdisciplinary approach that will transform coastal flood hazard assessments, and improve societal preparedness and resilience to flooding. We will use this collaborative framework to seek external funding from federal (NSF, NOAA, FEMA, USACE) and state agencies.

Team Members

Qin Jim Chen

Qin Jim Chen

College of Engineering
Samuel Munoz

Samuel Muñoz

College of Science
Yun Raymond Fu

Y. Raymond Fu

College of Engineering