The NSF GRFP recognizes and supports outstanding graduate students in NSF-supported STEM disciplines who are pursuing research-based master's and doctoral degrees at accredited US institutions. The GRFP provides up to three years of support for the graduate education of individuals who have demonstrated their potential for significant achievements in science and engineering research. For the 2020 competition, NSF received over 13,000 applications and made approximately 2,000 fellowships offers.
Matthew Garcia, a Phd student in civil and environmental engineering, proposed the development of a next-generation flood alert system that will provide specially and temporally distributed flood inundation maps in near-real time. This breakthrough will come from the creation of a new machine learning application combined with a new hydrodynamic modeling setup. These new elements will allow for the prediction of floodplain maps directly from radar rainfall which under normal 2D modeling conditions takes tens of hours. This work will provide a low computational cost alternative for flood inundation modeling that can aid other fields like hazard assessment, regional planning, and emergency operations.