Learning Surrogate Rainfall-driven Inundation Models with Few Data

Flood hazard assessment demands fast and accurate predictions. Hydrodynamic models are detailed but computationally intensive, making them impractical for quantifying uncertainty or identifying extremes. In contrast, machine learning surrogates can be rapid, but training on scarce simulated or observed extreme data can also be ineffective. This work demonstrates the development of an effective surrogate model for flood hazard prediction by initializing deep learning (ResNet-18) with ensemble-approximated Conditional Gaussian Processes (EnsCGP) and finalizing it with a bias correction. The proposed methodology couples EnsCGP with a ResNet-18 architecture to estimate flood depth and uses ensemble optimal estimation for bias correction. The surrogate model was trained and evaluated using rainfall data from Daymet and hydrodynamic simulations from LISFLOOD-FP, spanning the period from 1981 to 2019. The training involved using data up to a certain year and testing on the subsequent year, iteratively progressing through the dataset. This process required approximately 100 training iterations and extensive data. Inundation depths are estimated rapidly at runtime (approximately 0.006 seconds per event). Results over multiple years in the current climate over Chicago demonstrate an average R-squared greater than 0.96, with median relative errors in flood depth estimates of about 1 percent.