A cloud-based flood warning system for forecasting impacts to transportation infrastructure systems. (September 2018)

• Record Type: Journal Article
• Authors: Morsy, Mohamed M.; Goodall, Jonathan L.; O'Neil, Gina L.; Sadler, Jeffrey M.; Voce, Daniel; Hassan, Gamal; Huxley, Chris
• Is Part Of: Environmental modelling & software. Volume 107(2018)
• Page Start: 231
• Page End: 244
• Publication Date: 2018-09
• Subjects: Flood warning applications -- Cloud computing -- 2D hydrologic model -- GPUs -- Amazon web services -- Reproducibility; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.05.007
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: Design of a cloud-based flood warning decision support system. Prototype of a cloud-based end-to-end system using Amazon Web Services (AWS). Produces near real-time results while strategically consuming resources. Automates access to forecasted rainfall, 2D model run, and output visualization. Uses GPUs to speedup 2D model run time by up to 80x compared to using a CPU.