Search Data.gov
Found 1659 dataset(s) matching "machine".
-
Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of Minnesota and Wisconsin. Process-Based (PB) models...
-
<p>This data release component contains model inputs including river basin attributes, weather forcing data, and simulated and observed river discharge.</p>
-
<p>This data release component contains model code and configurations for the LSTM models used to predict stream temperature.</p>
-
<p>This data release item contains water temperature predictions for 455 river sites across the U.S. Predictions are from the models described by Rahmani et al. (2021b).</p>
-
<p>This data release component contains model inputs including river basin attributes, weather forcing data, and simulated and observed river discharge.</p>
-
<p>This data release component contains mean daily stream water temperature observations, retrieved from the USGS National Water Information System (NWIS) and used to train and validate all...
-
<p>This data release component contains a shapefile of monitoring site locations coincident with the outlets of the 118 river basins modeled by Rahmani et al. (2020).<\p>
-
This dataset includes evaluation data ("test" data) and performance metrics for water temperature predictions from multiple modeling frameworks. Process-Based (PB) models were configured and...
-
Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of Minnesota and Wisconsin. Process-Based (PB) models...
-
<p>This data release provides all data and code used in Rahmani et al. (2020) to model stream temperature and assess results. Briefly, we used a subset of the USGS GAGES-II dataset as a test...
-
<p>Several models were used to improve water temperature prediction in the Delaware River Basin. <p>PRMS-SNTemp was used to predict daily temperatures at 456 stream reaches in the...
-
Synthetic training and test datasets for experiments on deep generative modeling of noise time series. Consists of data for the following noise types: 1) band-limited thermal noise, i.e.,...
-
Process-guided deep learning water temperature predictions: 5a Lake Mendota detailed prediction data
Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of Minnesota and Wisconsin. Process-Based (PB) models...
-
This dataset includes model inputs including gridded weather data, a stream network distance matrix, stream reach attributes and metadata, and reservoir characteristics.
-
<p>This data release provides the predictions from stream temperature models described in Chen et al. 2021. Briefly, various deep learning and process-guided deep learning models were built...
-
This dataset includes evaluation data ("test" data) and performance metrics for water temperature predictions from multiple modeling frameworks. Process-Based (PB) models were configured and...
-
This dataset includes model parameters and metadata used to configure models.
-
Concentrations of 11 species are reported from continuous measurements taken during a wintertime field study in Utah. Time series data for measured species generally displayed strong diurnal...
-
This data set contains a total of 73,031 landmarks. 10,433 landmarks were detected and extracted from 180 HiRISE browse images, and 62,598 landmarks were augmented from 10,433 original landmarks....
-
<p>Observations related to water and thermal budgets in the Delaware River Basin. Data from reservoirs in the basin include reservoir characteristics (e.g., bathymetry), daily water levels,...
