Discriminator‐Guided Generative Adversarial Networks for Urban Flood Prediction

Abstract

Flood modeling is crucial in flood management as it can provide early warnings and support informed decision‐making on mitigation and adaptation strategies. However, it remains challenging to provide accurate flood predictions in real time using hydrodynamic flood models due to high computational demands. This study presents a new discriminator‐guided Generative Adversarial Neural Networks (GANs) model for two‐dimensional, high‐resolution urban flood prediction. Compared with the traditional GANs, the role of the discriminator is re‐defined by modifying its structure and loss function, enabling pixel‐wise discrimination based on errors, thereby better meeting the requirements of high‐resolution flood prediction. The proposed model is tested on the case study of Exeter, which covers an area of 27 km² with a spatial resolution of 2 m, compared with the baseline models of Pix2Pix and U‐Net. The proposed model can accurately predict the water depths across historical and design rainfall events, achieving an average root mean square error of 0.044 m and Critical Success Index of 0.754, demonstrating the generalization capability on unseen rainfall events. The proposed model significantly improves computational efficiency and offers a viable solution for spatiotemporal flood prediction in real‐time, providing informed decision‐making for urban flood management.