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Deep learning (DL) models are a promising complement to hydrodynamic models. However, the application of DL for detailed predictions in large domains has not yet been tested. We aim to narrow this gap by improving flood mapping resolution derived from large coarse flood models. We used cGAN-Flood, a conditional generative adversarial-based model (cGAN), that showed satisfactory generalization. We demonstrate the applicability of cGAN-Flood by coupling it with mesh- and raster-based coarse models. A Hydrologic Engineering Center (HEC) River Analysis System (RAS) model (cell size of 32 m), which is mesh-based, was created for a 350 km2 watershed. In contrast, a HydroPol2D model, raster-based, was created for a 150 km2 watershed with a 15 m pixel size. We evaluated our method's performance against a 3 m resolution HEC-RAS model in seven catchments across the cities of San Antonio and Sao Paulo. Results indicate the cGAN-Flood improved flood map accuracy, illustrating how DL can enhance flood mapping resolution. [ABSTRACT FROM AUTHOR]

Two-dimensional hydrodynamic models are computationally expensive. This drawback can limit their application to solving problems requiring real-time predictions or several simulation runs. Although the literature presented improvements in using Deep Learning as an alternative to hydrodynamic models, Artificial Neural Networks applications for flood prediction cannot satisfactorily predict floods for areas outside the training datasets with different boundary conditions. In this paper, we used a conditional generative adversarial network (cGAN) aiming to generalize flood predictions in catchments not included in the training process. The proposed method, called cGAN-Flood, uses two cGAN models to solve a rain-on-grid problem by first identifying wet cells and then estimating the water depths. The cGANs were trained using HEC-RAS outputs as ground truth. cGAN-Flood distributes a target flood volume (v t) in a given catchment, which can be calculated via water balance from hydrological simulations. Our approach was trained on ten and tested on five urban catchments with distinct characteristics. The cGAN-Flood was compared to HEC-RAS for different rainfall magnitudes and surface roughness. We also compared our approach to the Weighted Cellular Automata 2D (WCA2D), a rapid flood model (RFM) used for rain-on-grid simulations. Our method successfully predicted water depths in the testing areas, showing that cGAN-Flood could generalize to different locations. However, cGAN-Flood tended to underestimate depths in channels in some areas for events with a small peak of precipitation intensity. cGAN-Flood was 50 and 250 times faster than WCA2D and HEC-RAS, respectively. Due to its computational efficiency and accuracy, we suggest that cGAN-Flood can be applied when fast simulations are necessary, and it can be a viable modeling solution for flood forecasts in large-scale watersheds. • We develop cGAN-Flood, a rapid flood model based on conditional generative adversarial networks. • cGAN-Flood combines deep learning with water balance to identify flooded areas and estimate water depths. • The proposed model can generalize to unseen catchments located outside of the training data and different rainfall events. [ABSTRACT FROM AUTHOR]