Generation and selection of training events for surrogate flood inundation models

The destructive and life-threatening nature of flood events calls for fast and accurate methods to predict dynamic flood behaviour. Data-driven surrogate models have been developed to quickly predict flood inundation, though their accuracy relies on the available flood information for model training and validation. Flood observations are rarely available at high spatial and temporal scales, and thus computationally expensive high-resolution hydrodynamic (high-fidelity) models are often used to generate training data through simulation of selected flood events. Given finite resources, only a limited number of events can be simulated using a high-fidelity model. However, there is no established approach for selecting representative and informative flood events to ensure that the surrogate model is robustly trained. In this study, a novel systematic approach for selecting flood events for the training of surrogate flood inundation models is introduced. The approach generates a large set of candidate events using a computationally efficient low-resolution hydrodynamic (low-fidelity) model and then selects training events based on the simulated spatial-temporal inundation depths of the candidate events. The approach is used to train surrogate models to predict flood inundation in three distinct case studies with different boundary conditions and topographies. The results show robust performance of the surrogate models developed with RMSE