Streamflow Predictions (2006-2014) from LSTM Models in Water- and Energy-limited Regions in the United States
April 22, 2024
The data set includes the daily streamflow predictions from (Long Short-Term Memory) LSTM models for 45 basins (27 basins in New England region and 18 basins in Great Basin region) in contrasting hydroclimate regions (water-limited Great Basin region and energy-limited New England region) in the United States. Also, the shapefiles of study basins and hydroclimate regions, and data to support the statistical results, figures, and tables are included.
Citation Information
| Publication Year | 2024 |
|---|---|
| Title | Streamflow Predictions (2006-2014) from LSTM Models in Water- and Energy-limited Regions in the United States |
| DOI | 10.5066/P136FIVW |
| Authors | Kul Bikram (Contractor) Khand, Gabriel Senay |
| Product Type | Data Release |
| Record Source | USGS Asset Identifier Service (AIS) |
| USGS Organization | Earth Resources Observation and Science (EROS) Center |
| Rights | This work is marked with CC0 1.0 Universal |
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Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States
The application of Long Short-Term Memory (LSTM) models for streamflow predictions has been an area of rapid development, supported by advancements in computing technology, increasing availability of spatiotemporal data, and availability of historical data that allows for training data-driven LSTM models. Several studies have focused on improving the performance of LSTM models; however, few studie
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Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States
The application of Long Short-Term Memory (LSTM) models for streamflow predictions has been an area of rapid development, supported by advancements in computing technology, increasing availability of spatiotemporal data, and availability of historical data that allows for training data-driven LSTM models. Several studies have focused on improving the performance of LSTM models; however, few studie
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Kul Bikram Khand, Gabriel B. Senay