Improving pluvial flood mapping resolution of large coarse models with deep learning

Abstract

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.