Deep learning–enhanced emulation of hydrodynamic models for improved flood inundation prediction

Floods are among the most frequent and destructive natural disasters. Accurate predictions and timely warnings are critical for mitigating flood risk. However, flood prediction remains challenging due to limited availability of high-resolution data for model calibration and validation, high computational demands for near-real-time simulations, and large uncertainties surrounding sophisticated flood inundation modeling chain. This study focuses on improving riverine flood inundation predictions by leveraging artificial intelligence and machine learning algorithms to fuse data and models, accelerate computation, and automate end-to-end predictive workflow. We develop machine learning based postprocessors to correct systematic biases in hydrodynamic model outputs by learning from historical prediction errors. We also train and evaluate a hybrid Convolution Neural Network architecture coupled with a transformer to produce high-resolution inundation maps, combining local spatial feature extraction with long-range attention mechanisms to capture watershed-scale connectivity. Finally, we construct a surrogate of the fully physics-based GPU-enabled hydrodynamic model, Two-dimensional Runoff Inundation Toolkit for Operational Needs (TRITON) to generate rapid inundation simulations. Our results highlight strong tradeoffs between model complexity (standalone, hybrid, and surrogate modeling approaches), the size and quality of training datasets, available computational resources, and overall prediction accuracy, showing the pathway toward real-time flood inundation forecasting. Improved predictions of flood inundations can provide actionable insights to enhance emergency management, reduce disaster risk, and build community resilience.