Search Data.gov
Found 900 dataset(s) matching "learning models".
-
An extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate in shallow groundwater across the conterminous United States (CONUS). Nitrate was...
-
<p>Daily temperature predictions in the Delaware River Basin (DRB) can inform decision makers who can use cold-water reservoir releases to maintain thermal habitat for sensitive fish and...
-
This dataset contains a comparison of packet loss counts vs handovers using four different methods: baseline, heuristic, distance, and machine learning, as well as the data used to train a machine...
-
The Annual Mean PM2.5 Components (EC, NH4, NO3, OC, SO4) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019, v1 data set contains annual predictions of the chemical...
-
<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...
-
Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning...
-
This metadata record describes model outputs and supporting model code for the Data-Driven Drought Prediction project of the Water Resources Mission Area Drought Program. The data listed here...
-
The Delaware River Basin (DRB) covers portions of five states (Delaware, Maryland, New Jersey, New York, and Pennsylvania) and several geologic provinces, encompassing much of the complex geology...
-
The Annual Mean PM2.5 Components Trace Elements (TEs) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019, v1 data set contains annual predictions of trace elements...
-
Daily lake surface temperatures estimates for 185,549 lakes across the contiguous United States from 1980 to 2020 generated using an entity-aware long short-term memory deep learning model....
-
Daily lake surface temperatures estimates for 185,549 lakes across the contiguous United States from 1980 to 2020 generated using an entity-aware long short-term memory deep learning model....
-
CLAIRE MONTELEONI*, GAVIN SCHMIDT**, AND SHAILESH SAROHA*** Climate models are complex mathematical models designed by meteorologists, geophysicists, and climate scientists to simulate and...
-
These two reports from the University of Pittsburgh document related efforts under Utah FORGE Project 2-2439v2 to estimate in-situ stresses in well 16B(78)-32 using laboratory data, machine...
-
Git archive containing Python modules and resources used to generate machine-learning models used in the "Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the...
-
The data are comprised of input and output data from Machine Learning models that were developed to predict watershed health (WH) values in HUC-10 sub-watersheds within three major Midwest river...
-
<p>Please visit the <a href='https://storymaps.arcgis.com/stories/0c6bb2b0a38243ccb5b1c36798bb6d63' target='_blank' rel='nofollow ugc noopener noreferrer'>User Guide</a> to learn about using the...
-
The 2020-2021 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent...
-
The 2021-2022 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent...
-
Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the...
-
As described in the README.md file, the GitHub repository github.com/USEPA/PRTR-QSTR-models/tree/data-driven are Python scripts written to run Quantitative Structure–Transfer Relationship (QSTR)...
