Deep vision-based framework for coastal flood prediction under sea level rise and shoreline protection

The world's coastal regions are home to billions, yet they are also hotspots for climate change-induced adversities, such as sea level rise (SLR), which exacerbate coastal risks, increasing the likelihood and severity of floods. To assess these risks and inform adaptation measures, coastal engineers require predictive models capable of producing accurate, high-resolution flood maps under the given projected climate change impacts and potential mitigation strategies (e.g., construction of seawalls). While traditional physics-based hydrodynamic simulators can deliver accurate results, their application incurs massive computational burdens. On the other hand, supervised Deep Learning (DL) techniques offer immense potential for creating data-driven surrogates that are orders of magnitude faster than their physics-based counterparts. Nevertheless, training of such models typically demands large amounts of annotated samples, and hence numerous time-consuming hydrodynamic simulations. To remove this barrier, we devise a vision-based framework that enables the training of performant DL-based surrogates in low-data settings. Leveraging the proposed framework, we develop several such models, among which two adapted from well-known medical image segmentation models (SWIN-Unet and Attention U-Net), to predict flood depths along the entire coast of Abu Dhabi under varying shoreline protection scenarios and an SLR of 0.5 meters. Additionally, we design a lightweight Convolutional Neural Network (CNN) model, termed CASPIAN, tailored specifically for the coastal flood prediction problem at hand. The flood maps produced by CASPIAN closely and consistently matched those from a physics-based simulator (on average, with around 97% of predicted floodwater levels having an absolute error of at most 10 cm.), while offering [Formula: see text] times faster inference speed. Lastly, we provide a dataset of synthetic high-resolution (up to 30 m. horizontal resolution within urban areas) flood depth maps for the coast of Abu Dhabi, which can serve as a benchmark for evaluating future coastal flood prediction models. The complete source code of the proposed framework is open-sourced at https://github.com/Arnukk/CASPIAN .