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Found 13 dataset(s) matching "empirical machine learning".
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The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes,...
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The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER - Empirical Deep...
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This is the metadata associated with Pavlovic et al. (2023) entitled "Empirical nitrogen and sulfur critical loads of U.S. tree species and their uncertainties with machine learning"...
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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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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...
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A three-dimensional extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate in groundwater across the conterminous United States (CONUS)....
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<div style='text-align:Left;'><div><div><p><span>This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS change attribution classes for each year. See...
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This data release contains model inputs, R code, and model outputs for predicting depth to bedrock in the Delaware River Basin at a 1km gridded resolution with a random forest model. Model inputs...
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This metadata record describes observed and predicted baseflow recession characteristics for 300 streamflow gauges in the western United States and 282 streamflow gauges in the eastern United...
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Global sea surface temperature (SST) anomalies can affect terrestrial precipitation via ocean-atmosphere interaction known as climate teleconnection. Non-stationary and non-linear characteristics...
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Empirical models described in previous publications were developed and applied to estimate the probability of streamflow modification for every stream segment in the conterminous United States...
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This paper takes an empirical approach to identify operational factors at busy airports that may predate go-around maneuvers. Using four years of data from San Francisco International Airport,...
