Accurate flood prediction is essential for effective risk management, but simulating with physics-based models is computationally intensive and time-consuming, limiting their use in operational use cases. To address this challenge, this study develops a deep learning surrogate model that replicates spatial flood depth predictions of the physics-based model SFINCS, with significantly reduced computational cost.

The surrogate model is trained with a synthetic dataset of 13,000 tracks generated by TCWiSE, that have been computed with SFINCS. Input for the surrogate model are key drivers of compound coastal flooding, including cumulative precipitation, maximum wind speed, and water depth at the river outlet, which are given to the model as scalar values. The results demonstrate that the model can reproduce flood depth patterns with a RMSE of 0.054 m on the original dataset and 0.069 m on a case study application. The CSI values of 0.829 and 0.776, respectively, are comparable to values reported in recent literature. Visual inspection of spatial predictions shows that the model performs well overall but shows localized error patterns, particularly in the western part of the domain, indicating that additional input features may be needed to better capture interactions in physical processes that occur in compound coastal flooding. From a computational perspective, the deep learning model runs approximately 100 times faster than the physics-based model SFINCS on CPU, with further gains on GPU. This computational efficiency allows for running more simulations within a given timeframe, enabling the exploration of an increased number of potential flood scenarios and providing decision-makers with more time to evaluate and respond.

Overall, the study demonstrates that deep learning surrogate models offer a promising alternative to physics-based models by providing accurate and faster predictions. Although the current set-up shows accurate results, there remains room for improvement through enhanced input representation, such as incorporating spatiotemporal information of the drivers or including additional hydrodynamic features. Further gains in performance and generalizability can also be achieved by refining the model architecture and training strategies.

In this project, student teams from Universidad San Francisco de Quito and Delft University of Technology worked together to provide safe drinking water in Ecuador. We used M-100 chlorinators from the company WaterStep, donated by Water Ambassadors Canada, to chlorinate water in schools and communities. The journey involved testing water quality, setting up the chlorination systems, and learning how to work in different communities. The key to success was testing water properly and setting up the system carefully. Learnings about working with local communities and organizations are included as well as the importance to understand each community’s needs to make a real difference.