Advancing Flood Analytics and Forecasting through Flood Computing and Engineered System (FloodEngine)

Floods pose considerable threats to human life, critical infrastructure, and the environment, yet current flood modeling architectures are not effective in providing reliable information for informed emergency management and response. To address this need, this project will leverage smart cyber-physical systems (SCPS) capabilities to develop FloodEngine, a next-generation flood computing system that will forecast floods in near real-time by drawing from data engineering, water science, and deep learning algorithms.

Focusing on South Carolina as a testbed, this project will address three objectives: (i) optimize data gathering and processing; (ii) develop a physics-based deep learning model; and (iii) promote knowledge transfer through innovative networks. The deep integration of SCPS research and training plans will provide flexible mechanisms to be leveraged for enhancing flood analytics research and engaging learners in more creative and interactive computational learning experiences.

This project will train the next generation of flood modelers and strengthen flood analytics and technical design. When built, FloodEngine will advance the frontiers of flood research, ensure accessibility and reproducibility, and build an active user community to catalyze scientific discovery across a broad range of fields. Furthermore, users will be involved in the FloodEngine training workshop to create a community of flood modeling developers.

The project will leverage resources of the Clemson inclusion programs to mentor students in engineering and recruit and broaden the participation of flood researchers in the project. This project is designed as a collaboration with the National Weather Service, the South Carolina Office of Resilience, and the Consortium of Universities for the Advancement of Hydrologic Science, Inc., among others.

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.