Search Data.gov
Found 900 dataset(s) matching "learning models".
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This is the supporting data used to train machine learning models used by the National Earthquake Information Center to improve pick times and classify source characteristics.
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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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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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A series of machine learning (ML) models were developed for Minnesota. The ML models were trained and tested using suspended sediment, bedload, streamflow, and geospatial data to predicted...
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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...
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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...
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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...
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A labeled dataset of Landsat, Sentinel, and Planetscope satellite visible-band images of coastal shoreline regions, consisting of folders of images that have been labeled as either suitable or...
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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...
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NOTE: This dataset pertains only to the 2020-2021 school year and is no longer being updated. For additional data on COVID-19, visit data.ct.gov/coronavirus. This dataset includes the leading...
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This page provides access to data on the key metrics developed by the Connecticut Department of Health (DPH) and the Department of Education (CSDE) to support local district decision-making on the...
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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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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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Groundwater is a vital resource in the Mississippi embayment physiographic region (Mississippi embayment) of the central United States and can be limited in some areas by high concentrations of...
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Groundwater from the Mississippi River Valley alluvial aquifer (MRVA) is a vital resource for agriculture and drinking-water supplies in the central United States. Water availability can be...
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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 groundwater salinity—including...
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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 groundwater salinity—including...
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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 groundwater salinity—including...
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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 groundwater salinity—including...
