Found 13 datasets matching "empirical machine learning".
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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"...
Search relevance: 90.08 | Views last month: 7 -
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,...
Search relevance: 77.41 | Views last month: 2 -
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...
Search relevance: 74.97 | Views last month: 26 -
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...
Search relevance: 73.29 | Views last month: 0 -
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...
Search relevance: 68.19 | Views last month: 0 -
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...
Search relevance: 68.19 | Views last month: 0 -
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,...
Search relevance: 66.82 | Views last month: 3 -
This dataset provides six global gridded products at 1-km resolution of predicted annual soil respiration (Rs) and associated uncertainty, maps of the lower and upper quartiles of the prediction...
Search relevance: 66.56 | Views last month: 0 -
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...
Search relevance: 52.26 | Views last month: 0 -
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...
Search relevance: 52.01 | Views last month: 2 -
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)....
Search relevance: 51.23 | Views last month: 0 -
Global sea surface temperature (SST) anomalies can affect terrestrial precipitation via ocean-atmosphere interaction known as climate teleconnection. Non-stationary and non-linear characteristics...
Search relevance: 47.64 | Views last month: 4 -
Landscape Change Monitoring System (LCMS) Conterminous United States Cause of Change (Image Service)
Note: This LCMS CONUS Cause of Change image service has been deprecated. It has been replaced by the LCMS CONUS Annual Change image service, which provides updated and consolidated change...
Search relevance: 30.25 | Views last month: 0
