A cloud-based flood warning system for forecasting impacts to transportation infrastructure systems

The ability to quickly and accurately forecast flooding is increasingly important as extreme weather events become more common. This work focuses on designing a cloud-based real-time modeling system for supporting decision makers in assessing flood risk. The system, built using Amazon Web Services (AWS), automates access and pre-processing of forecast data, execution of a computationally expensive and high-resolution 2D hydrodynamic model, Two-dimensional Unsteady Flow (TUFLOW), and map-based visualization of model outputs. A graphical processing unit (GPU) version of TUFLOW was used, resulting in an 80x execution time speed-up compared to the central processing unit (CPU) version. The system is designed to run automatically to produce near real-time results and consume minimal computational resources until triggered by an extreme weather event. The system is demonstrated for a case study in the coastal plain of Virginia to forecast flooding vulnerability of transportation infrastructure during extreme weather events.