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Random forest regression model and prediction rasters of fluoride in groundwater in basin-fill aquifers of western United States

Dates

Publication Date
Start Date
2000-10-01
End Date
2018-07-01

Citation

Rosecrans, C.Z., Ransom, K.M, and Rodriquez, O., 2021, Random forest regression model and prediction rasters of fluoride in groundwater in basin-fill aquifers of western United States: U.S. Geological Survey data release, https://doi.org/10.5066/P991L1ZR.

Summary

A random forest regression (RFR) model was developed to predict groundwater fluoride concentrations in four western United Stated principal aquifers —California Coastal basin-fill aquifers, Central Valley aquifer system, Basin and Range basin-fill aquifers, and the Rio Grande aquifer system. The selected basin-fill aquifers are a vital resource for drinking-water supplies. The RFR model was developed with a dataset of over 12,000 wells sampled for fluoride between 2000 and 2018. This data release provides rasters of predicted fluoride concentrations at depth typical of domestic and public supply wells in the selected basin-fill aquifers and includes the final RFR model that documents the prediction modeling process and verifies and [...]

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Attached Files

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Supporting_GIS_Information.csv 3.71 KB text/csv
WesternUS_fluoride_randomForest.zip 41.99 MB application/zip
read_me.txt 4 KB text/plain

Purpose

The random forest model predictions and rasters of fluoride concentrations in groundwater support the manuscript by Rosecrans and others (2021). The work is part of the U.S. Geological Survey's National Water Quality Assessment Project, a component of the National Water Quality Program.

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Communities

  • USGS California Water Science Center
  • USGS Data Release Products

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Type Scheme Key
DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/P991L1ZR

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