-
Federal
Utah FORGE 6-3629: Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation - 2025 Workshop Presentation
Department of Energy —
This is a presentation on the Cutting Edge Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation by the... -
Federal
Weekly cloud free Harmonized Landsat Sentinel (HLS) Normalized Difference Vegetation Index (NDVI) estimates for western United States (2016 – 2019).
Department of the Interior —
In support of mapping ecological conditions (e.g. invasive annual grass) in sagebrush-dominated landscapes of the western United States, we developed weekly (starting... -
Federal
Daily water column temperature predictions for thousands of Midwest U.S. lakes between 1979-2022 and under future climate scenarios
Department of the Interior —
Lake temperature is an important environmental metric for understanding habitat suitability for many freshwater species and is especially useful when temperatures are... -
Federal
Redox zone rasters of groundwater in the Mississippi River Valley alluvial and Claiborne aquifers
Department of the Interior —
Groundwater is a vital resource in the Mississippi embayment physiographic region (Mississippi embayment) of the central United States and can be limited in some... -
Federal
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 4 Models
Department of the Interior —
This data release component contains model code and configurations for the LSTM and linear regression models used to predict stream temperature. -
Federal
Depth rasters in aquifers of the Mississippi embayment
Department of the Interior —
Groundwater is a vital resource in the Mississippi embayment of the central United States. An innovative approach using machine learning (ML) was employed to predict... -
Federal
Drinking Water Microbiome Sequence Data Set
U.S. Environmental Protection Agency —
The fasta file (BM_OTU.fasta) contain the sequences of the bacterial 16S rRNA-encoding V4 region gene (≈250 nt) for each Operational Taxonomic Unit (OTU). This... -
Federal
Delaware River Basin Stream Salinity Machine Learning Models and Data
Department of the Interior —
This model archive contains the input data, model code, and model outputs for machine learning models that predict daily non-tidal stream salinity (specific... -
Federal
Predicting water temperature in the Delaware River Basin: 2 Water temperature and flow observations
Department of the Interior —
Observations related to water and thermal budgets in the Delaware River Basin. Data from reservoirs in the basin include reservoir characteristics (e.g., bathymetry),... -
Federal
Predicting compound amenability with liquid chromatography-mass spectrometry to improve non-targeted analysis
U.S. Environmental Protection Agency —
The dataset and experimental and predicted amenability calls are provided in the supplemental file “Supplemental_ToxCast_PhaseII.xlsx”. PaDEL descriptors were... -
Federal
Lithium observations, machine-learning predictions, and mass estimates from the Smackover Formation brines in southern Arkansas
Department of the Interior —
Global demand for lithium, the primary component of lithium-ion batteries, greatly exceeds known supplies and this imbalance is expected to increase as the world... -
Federal
INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements
Department of Energy —
The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design... -
Federal
Utah FORGE: Source Imaging DAS-Based Seismic Event Catalog - April 2024 Stimulation
Department of Energy —
This catalog contains microseismic event locations recorded during the April 2024 stimulation at the Utah FORGE site. Events were detected and located using a DAS-... -
Federal
Predictions for the presence of submersed aquatic vegetation in the upper Mississippi River, USA, from years 2010-2019
Department of the Interior —
The datasets are to accompany a manuscript describing the prediction of submersed aquatic vegetation presence and its potential vulnerability and recovery potential.... -
Federal
Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada
Department of Energy —
This submission contains geotiffs, supporting shapefiles and readmes for the inputs and output models of algorithms explored in the Nevada Geothermal Machine Learning... -
Federal
Predictive soil property map: Gypsum content
Department of the Interior —
These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado... -
Federal
Estimated floodplain map for the conterminous United States
U.S. Environmental Protection Agency —
Understanding the relationship between flood inundation and floodplains is critical for ecosystem and community health and well-being, as well as targeting floodplain... -
Federal
Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs
Department of the Interior —
This dataset includes model inputs that describe local weather conditions for Sparkling Lake, WI. Weather data comes from two sources: locally measured (2009-2017)... -
Federal
Machine-learning model predictions and rasters of arsenic and manganese in groundwater in the Mississippi River Valley alluvial aquifer
Department of the Interior —
Groundwater from the Mississippi River Valley alluvial aquifer (MRVA) is a vital resource for agriculture and drinking-water supplies in the central United States.... -
Federal
Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files
Department of Energy —
This data set includes the numerical modeling input files and output files used to synthesize data, and the reduced-order machine learning models trained from the...