Input and results from a random forest classification (RFC) model that predicts redox conditions in groundwater in the contiguous United States
January 23, 2024
This data release provides model inputs and outputs for a model that predicts redox conditions in groundwater in the contiguous United States. Input variables describe the hydrology, soils, geology, and hydrologic position of groundwater sample locations. The data release accompanies a journal article that describes model development and applications (Tesoriero_and_others_2023).
Citation Information
Publication Year | 2024 |
---|---|
Title | Input and results from a random forest classification (RFC) model that predicts redox conditions in groundwater in the contiguous United States |
DOI | 10.5066/P9DVPJIX |
Authors | Susan Wherry, Jim Tesoriero, Danielle I Dupuy |
Product Type | Data Release |
Record Source | USGS Asset Identifier Service (AIS) |
USGS Organization | Oregon Water Science Center |
Rights | This work is marked with CC0 1.0 Universal |
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Predicting redox conditions in groundwater at a national scale using random forest classification
Redox conditions in groundwater may markedly affect the fate and transport of nutrients, volatile organic compounds, and trace metals, with significant implications for human health. While many local assessments of redox conditions have been made, the spatial variability of redox reaction rates makes the determination of redox conditions at regional or national scales problematic. In this study, r
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Predicting redox conditions in groundwater at a national scale using random forest classification
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