Linked remote sensing and Long Short-Term Memory (LSTM) models reveal how surface water storage dynamics influence river discharge
L o a d i n g
Organization
United State Environmental Protection Agency - view all
Update frequencyunknown
Last updated4 weeks ago
Format
OverviewLSTM machine learningremote sensingsentinel time-seriessurface water storage
Linked remote sensing and Long Short-Term Memory (LSTM) models reveal how surface water storage dynamics influence river discharge. This dataset is not publicly accessible because: It belongs to external collaborators. It can be accessed through the following means: The DOI for the final data release will be: (https://doi.org/10.5066/P14WYWSY). Format: The data will be housed at USGS's sciencebase.gov with an FGDC metadata .xml file as well as a csv file have been included along with model results for both remote sensing at the SWAT model subbasins. A link should be included on ScienceHub that will direct users to USGS's sciencebase. The DOI for the final data release will be: (https://doi.org/10.5066/P14WYWSY)
Additional Information
KeyValue
Dcat Modified2025-03-11
Dcat Publisher NameU.S. EPA Office of Research and Development (ORD)
Guidhttps://doi.org/10.23719/1532080
Harvest Object Id239a7503-3e61-4674-9774-501b7176a973
Harvest Source Idb8e63f83-bbb9-45d3-a3de-09607cc9ff8a
Harvest Source TitleUSEPA Environmental Dataset Gateway
