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LSTM Efficacy in Runoff Prediction: A Study Using Spatial Datasets Across Diverse Meteorological Conditions Including Big Sandy River.

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Type: Resource
Storage: The size of this resource is 29.8 MB
Created: Mar 20, 2024 at 1:46 a.m.
Last updated: Mar 20, 2024 at 7 a.m.
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Content types: Geographic Feature Content  Geographic Raster Content 
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This resource comprises various files pertaining to time series data, particularly focusing on NWM (National Water Model) short-range forecast and USGS observations of streamflow data for three stations, measured in cubic feet per second (cfs). I added some spatial datasets in the form of vector and raster datasets just for one specific research area.
The contents of each file serve distinct purposes:
- "USGS Observation and NWM Outputs" is consisted of merged NWM forecast and USGS observation data;
-"Data types" highlights some information including coordinates and reach ID and gage ID for specific locations in Arizona, Nevada, and Wisconsin in the USA;
- "Results" showcases images associated with the statistical metrics for aforementioned locations, offering visual insights into data analysis outcomes;
-"Data Collection and Analysis" summarizes merged data from the NWM and USGS, accompanied by statistical metrics for analysis;
- "LSTM Paper" presents an incomplete paper on LSTM models application to the dataset, necessitating revision and completion in the near future;
-"Big Sandy watershed " includes Vector data (shapefiles) for the delineated watershed shapefiles.
-"Big Sandy streamlines" is consist of the stream lines for the specific watershed.
-"Big Sandy River " includes the raster data for the delineated watershed which contains big sandy river.

Subject Keywords



Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
North Latitude
East Longitude
South Latitude
West Longitude


Start Date:
End Date:


Data Services

The following web services are available for data contained in this resource. Geospatial Feature and Raster data are made available via Open Geospatial Consortium Web Services. The provided links can be copied and pasted into GIS software to access these data. Multidimensional NetCDF data are made available via a THREDDS Data Server using remote data access protocols such as OPeNDAP. Other data services may be made available in the future to support additional data types.

Additional Metadata

Name Value
Gage Type River gage
Spatial data GCS_WGS_1984 projection
NWM Forecasts Time series data
Analysis Periods April Month
USGS Observation Streamflow Time series data

Related Resources

This resource is described by Han, H., Morrison, R. R., (2022). Improved runoff forecasting performance through error predictions using a deep-learning approach. Journal of Hydrology (Elsevier)


Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
Cooperative Institute for Research to Operations in Hydrology (CIROH) Collaborative Research: Advancing Data Scienceand Analytics for Flood Predictions


People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.

Name Organization Address Phone Author Identifiers
Dan Ames Brigham Young University Utah, US (801) 422-3620 ResearchGateID , GoogleScholarID

How to Cite

Najafi, R., D. Ames (2024). LSTM Efficacy in Runoff Prediction: A Study Using Spatial Datasets Across Diverse Meteorological Conditions Including Big Sandy River., HydroShare,

This resource is shared under the Creative Commons Attribution CC BY.


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