Developing a Deep Learning-Based Super-Resolution Urban Flood Model: Towards Scalable and Reliable Hydrological Predictions

Integrating deep learning techniques into hydrology has opened a new way to improve urban flood modeling, with various solutions being developed to address urban flood problems driven by climate change and urbanization. However, predicting urban inundation in near real-time for large urban areas remains challenging due to computational demands and limited data availability. This work proposes a deep learning-based super-resolution framework that enhances the spatial resolution of process-based urban flood modeling outputs using convolutional neural networks (CNNs) while improving computational efficiency. This study investigates the interaction between deep learning model architecture and the underlying physical processes to improve prediction accuracy and robustness in urban pluvial flood mapping. The methodology will be applied to various urban flood scenarios, including extreme rainfall events and hurricane-induced flooding, and its performance will be evaluated through quantitative indicators and sensitivity analyses. The applicability and scalability of this model will also be discussed. In particular, strategies to enhance model reliability and integrate additional hydrological information under extreme conditions will be explored. The study will further address uncertainty estimation in deep learning-based super-resolution models and scalability challenges associated with super-resolution approaches for large-scale flood simulations. The findings aim to demonstrate the potential of deep learning as an innovative tool in hydrological modeling and to enable more effective flood risk management strategies.