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Hourly Stream Heights – A Short-term Deep Learning Prediction ModelRiver flooding can have a detrimental impact on a community by causing loss of life, loss or damage to property, and damage to infrastructure. Having the capability to forecast flooding events can prevent the loss of life and mitigate damage to property. A programmatic, data-driven approach using deep learning to forecast a stream’s gauge height every 6 hours has been developed by NASA’s Short-term Prediction Research and Transition Center (SPoRT) and is currently in operation. Based on our end user engagement and feedback, this medium-term product has been successfully adopted by several National Weather Service (NWS) Forecast Offices and River Forecast Centers (RFC). SPoRT is currently researching and developing a short-term hourly deep learning model which will be useful in forecasting flash flood events.
Document ID
20230009170
Acquisition Source
Marshall Space Flight Center
Document Type
Poster
Authors
Michael Antia
(Jacobs (United States) Dallas, Texas, United States)
Andrew T. White
(University of Alabama in Huntsville Huntsville, Alabama, United States)
Roger Allen
(Jacobs (United States) Dallas, Texas, United States)
Christopher R. Hain
(Marshall Space Flight Center Redstone Arsenal, Alabama, United States)
Kristopher D. White
(National Weather Service Silver Spring, Maryland, United States)
Date Acquired
June 16, 2023
Subject Category
Meteorology and Climatology
Meeting Information
Meeting: MSFC Science Jamboree and Poster Expo
Location: Huntsville, AL
Country: US
Start Date: June 22, 2023
Sponsors: Marshall Space Flight Center
Funding Number(s)
WBS: 281945.02.80.01.66
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
Technical Review
Single Expert
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