Rapid flood inundation mapping by integrating deep learning-based image super-resolution with coarse-grid hydrodynamic modeling

Efficient and accurate flood inundation mapping is essential for flood risk assessment, emergency response, and community safety. The deep learning-enabled rapid flood simulation demonstrates superior computational efficiency compared to traditional hydrodynamic models. However, most deep learning-based flood mapping models currently focus on predicting the maximum water depth and face challenges in generalizing rainfall events of different durations. This paper proposes a fast flood simulation method based on image super-resolution, utilizing a novel DenseUNet architecture to predict the maximum water depth and velocity of temporal rainfall events. The proposed method integrates physical catchment characteristics to enhance the resolution of flood maps generated by the coarse-grid hydrodynamic model using the deep-learning model. The method is applied to a rural-urban catchment of the Shenzhen River in southern China. The proposed method effectively reproduces maximum water depth and velocity for test rainfall events against the fine-grid hydrodynamic model, achieving root mean square errors below 0.06 and 0.07 m/s, respectively, with a percentage bias within (Formula presented.) 5%. For maximum water depth prediction, the method exhibits Nash-Sutcliffe efficiency and Pearson correlation coefficient exceeding 0.99. Similarly, for maximum velocity prediction, both metrics exceed 0.94. The computational efficiency of the proposed method outperforms the fine-grid hydrodynamic model by over 2800 times. The DenseUNet architecture developed in this study exhibits superior regression and classification performance compared to the commonly used ResUNet and UNet architectures. The proposed method is robust for a wide range of super-resolution scale factors. This paper presents an accurate and efficient surrogate model for rapid flood inundation mapping, providing valuable insights for applying deep learning-based image super-resolution methods in flood simulation.