Case, Jonathan L.

Hain, Christopher R.

White, Andrew T.

White, Kristopher D.

English

NASA Technical Reports Server

River Flood Prediction Using a Long Short-
Term Memory Recurrent Neural Network
Andrew T. White1, Kristopher D. White2, Christopher R. Hain3, Jonathan L. Case4
1University of Alabama in Huntsville/NASA MSFC SPoRT
2NWS/NASA MSFC SPoRT
3NASA MSFC SPoRT
4ENSCO, Inc./NASA MSFC SPoRT
https://ntrs.nasa.gov/search.jsp?R=20200000478 2020-03-11T12:41:13+00:00Z
Background
During approaching flooding 
events, NWS offices rely on their 
River Forecast Center (RFC) to 
produce river gauge height 
predictions.
Typically, precipitation is only 
accounted for in the forecast out to 
1 or 2 days.  
Forecasts are only available for 
deemed “forecast points”
Without rainfall forcing, 
forecasters have to rely on “rules 
of thumb”
RFC forecasts for the Flint River in Northern Alabama.
Flood Forecasting
Tropical Storm Lee case study
Heavy rainfall was forecasted over 
North Alabama.
Widespread river flooding was 
forecasted.
Very little flooding was observed.
Heavy rainfall over 
a 24-hr period.
Minor rise in 
gauge height. 
Importance of Soil Moisture
Rainfall alone is not enough to 
infer flood potential.
Soil moisture conditions control 
rainfall infiltration rates.  
Incorporation of SPoRT-LIS into 
flood forecasting.
Analyze pre-storm soil moisture 
conditions.
Very low antecedent 
soil moisture.
Forecasting Challenges Remain
While incorporation of soil moisture in flood forecasting has been 
beneficial, uncertainties remain.
Which soil layer is most important? 
0 – 10 cm, 10 – 40 cm, 40 – 100 cm, 100 – 200 cm, 0 – 200 cm
Are there a critical values at which flooding becomes more likely?
How fast does soil moisture change?
What type of soil is located in the basin of interest?
Machine Learning
Machine learning consists of finding statistical relationships to go 
from an input(s) to an output.
The developed relationships are learned from the data.
Input Layer Hidden 
Layers
Output Layer
Random Forest Neural Network
Input Data
Prediction 
1
Prediction 
2
Prediction 
N
Final 
Prediction
Tree 1 Tree 2 Tree N
Long short-term memory (LSTM) model
Type of Recurrent Neural 
Network (RNN)
Composed of several, connected  
networks which are time-
dependent.
Variation of on the traditional RNN 
designed to use time dependent 
data more effectively. 
Example LSTM Diagram
LSTM Model Input
The LSTM model was trained using 
available data over the Jan. 2010 –
Dec. 2018.
 Input time-lagged features: 
 SPoRT LIS Relative Soil Moisture (0 – 10 
cm, 10 – 40 cm, 
40 – 100 cm, 100 – 200 cm, 0 – 200 cm)
Gauge Height
MRMS 6hr QPE for training (any QPF can 
be used for forecasting)
t+6tt-6t-12t-18
Gauge Height
Soil Moisture
6-hr Precipitation
t-24
Project Area
The Geospatial Attributes of Gages 
for Evaluating Streamflow, version 
II, (GAGES-II) produced by USGS 
was used to get gauge and basin 
delineation shapefiles.
1,796 basins within the southeast 
U.S. were chosen for initial testing.
Defined basin boundaries are used 
to take areal averages of rainfall 
and soil moisture. Southeast U.S. Domain
Operational Forecasts
5 day forecast of gauge height are 
being produced 4 times a day (0, 6, 
12, 18 UTC).
Gauge height is predicted every 6 six 
hours within the 5 day period.
Multiple models are run with 
different QPF (GFS, WPC) forcing. 
Basin average precipitation is overlaid 
to allow forecasters to observe the 
model response to different forcing.



Validation
Preliminary validation was done over the January 2019 – May 2019 
time period.
Mean bias and mean absolute error were calculated based on this time 
period.
Summary
The LSTM approach presented here can be applied to any gauged 
river basin.
This allows NWS WFOs to have forecasts for basins that are not provided by 
their River Forecast Center (RFC).
The developed model provides long-term forecasts that account for 
rainfall throughout.
While the model has shown to have low error, it is only as good as 
the input QPF.
Forecasters must account for QPF uncertainties when looking at the output.  
Thank you!
andrew.t.white@nasa.gov


River Flood Prediction Using a Long Short-Term Memory Recurrent Neural Network

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