Improving Urban Flood Predictability Using Deep Learning-Based Radar Rainfall Nowcasting and an Inundation Model

Due to the impact of climate change, the likelihood of short-duration intense rainfall events is increasing. In particular, urban areas are particularly vulnerable to cascading and compounding flood impacts when torrential rains occur. Therefore, to effectively respond to urban flooding, it is crucial to produce rapid inundation information alongside real-time rainfall forecasting. However, existing physics-based flood models have limitations in real-time prediction due to long computation times and high dependence on detailed topographic and drainage network data. To address these limitations and enhance flood prediction accuracy, this study utilized a deep learning-based flood prediction model (CRU-Net) and radar-based rainfall nowcasting.

CRU-Net is a U-Net-based deep learning model designed to combine Residual Blocks and Convolutional Block Attention Module (CBAM) to simultaneously capture the spatiotemporal variability of rainfall and the complex characteristics of urban topography. Furthermore, by utilizing input parameters of physics-based flood models such as topography, land cover, and drainage networks, it serves as a surrogate model for flood prediction that reflects the physical structure of urban watersheds. In this study, CRU-Net was trained using flood scenarios generated with SWMM and a 2D flood analysis model. Additionally, to secure a lead time for flood prediction, the KICT-RAIN-AI model was used to generate predicted rainfall with a lead time of 10 to 180 minutes.

The study area was Gwanak-gu, Seoul, South Korea, which suffered flood damage in August 2022. Rainfall occurring between August 8 and 9, 2022, was predicted, and flood depths were predicted at 10-minute intervals. Based on the time-series prediction results, flood inundation extent maps were created, and flood reproducibility was examined by comparing them with the actual flood trace maps from 2022. This study demonstrates the potential to overcome the limitations of conventional physics-based approaches and to secure the lead time necessary for evacuation during urban flood events

Acknowledgements:
This work is financially supported by Korea Ministry of Climate, Energy, Environment(MCEE) as Climate Resilient R&D Project for Water-Related Disaster Management (RS-2026-25502323).