Rapid Flood Inundation Forecast Using Fourier Neural Operator
Alexander Y. Sun Zhi Li Wonhyun Lee Qixing Huang Bridget R. Scanlon Clint Dawson
Flood inundation forecast provides critical information for emergency
planning before and during flood events. Real time flood inundation forecast
tools are still lacking. High-resolution hydrodynamic modeling has become more
accessible in recent years, however, predicting flood extents at the street and
building levels in real-time is still computationally demanding. Here we
present a hybrid process-based and data-driven machine learning (ML) approach
for flood extent and inundation depth prediction. We used the Fourier neural
operator (FNO), a highly efficient ML method, for surrogate modeling. The FNO
model is demonstrated over an urban area in Houston (Texas, U.S.) by training
using simulated water depths (in 15-min intervals) from six historical storm
events and then tested over two holdout events. Results show FNO outperforms
the baseline U-Net model. It maintains high predictability at all lead times
tested (up to 3 hrs) and performs well when applying to new sites, suggesting
strong generalization skill.