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Found 573 dataset(s) matching "machine learning model".
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The open dataset, software, and other files accompanying the manuscript "An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty...
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This model archive contains the input data, model code, and model outputs for machine learning models that predict daily non-tidal stream salinity (specific conductance) for a network of 459...
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This child item describes a public-supply delivery machine learning model that was developed to estimate public-supply deliveries. Publicly supplied water may be delivered to domestic users or to...
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We applied machine learning (ML) models to forecast streamflow drought from 1 to 13 weeks into the future at more than 3,000 streamgage locations across the Conterminous United States. We applied...
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This dataset contains 3,229 feather files with time series of all model inputs for machine learning models predicting streamflow drought across the conterminous United States (CONUS). Files...
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This submission contains geotiffs, supporting shapefiles and readmes for the inputs and output models of algorithms explored in the Nevada Geothermal Machine Learning project, meant to accompany...
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Data and preliminary machine-learning models used to predict manganese and 1,4-dioxane in groundwater on Long Island are documented in this data release. Concentration data used to develop the...
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A machine learning streamflow (MLFLOW) model was developed in R (model is in the Rscripts folder) for modeling monthly streamflow from 2012 to 2017 in three watersheds on the Wyoming Range in the...
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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 synthesized data for reservoir...
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Salinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water supplies. Current state-of-the-art...
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The data are a set of fluorescent images that were generated to support the development of a machine learning model. The approach combines fluorescence imaging, deep learning, a mobile...
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This child item describes a machine learning model that was developed to estimate public-supply water use by water service area (WSA) boundary and 12-digit hydrologic unit code (HUC12) for the...
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Data and code for "Dawson, D.E.; Lau, C.; Pradeep, P.; Sayre, R.R.; Judson, R.S.; Tornero-Velez, R.; Wambaugh, J.F. A Machine Learning Model to Estimate Toxicokinetic Half-Lives of Per- and...
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A dataset of semantic segmentations of Landsat, Sentinel, and Planetscope satellite images of coastal shoreline regions, consisting of folders of images that have been labeled as either suitable...
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ml_uncertainty: A Python module for estimating uncertainty in predictions of machine learning models
This software is a Python module for estimating uncertainty in predictions of machine learning models. It is a Python package that calculates uncertainties in machine learning models using...
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This is the geospatial and hydroclimate input data used to develop data-driven Machine Learning (ML) models as well as model estimated water quality based risk metrics and watershed health...
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These data represent an annotated training data for machine learned life history classification of the daily activity of dabbling ducks (f. Anatidae sf. Anatinae) using hourly GPS data. Each row...
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Enhanced geothermal systems (EGS) can provide a sustainable and renewable solution to the new energy transition. Its potential relies on the ability to create a reservoir and to accurately...
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Boosted regression trees (BRT), a type of ensemble-tree machine-learning method, were used to predict specific conductance concentration at multiple depths throughout the Mississippi River Valley...
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Due to the increasing diversity of organic contaminants discharged into anoxic water environments, reactivity prediction is necessary for chemical persistence evaluation for water treatment and...
