Machine-learning model predictions and rasters of arsenic and manganese in groundwater in the Mississippi River Valley alluvial aquifer
December 3, 2021
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 limited in some areas of the aquifer by high concentrations of trace elements, including manganese and arsenic. Boosted regression trees, a type of ensemble-tree machine-learning method, were used to predict manganese concentration and the probability of arsenic concentration exceeding a 10 µg/L threshold throughout the MRVA. Explanatory variables for the BRT models included attributes associated with well location and construction, surficial variables (such as hydrologic position and recharge), variables extracted from a MODFLOW-2005 groundwater-flow model for the Mississippi embayment, and variables from an airborne electromagnetic survey of the aquifer. This data release provides the R scripts to tune and reproduce the BRT models and final prediction rasters. For a full description of modeling workflow and final model selection see the companion journal article.
Citation Information
Publication Year | 2021 |
---|---|
Title | Machine-learning model predictions and rasters of arsenic and manganese in groundwater in the Mississippi River Valley alluvial aquifer |
DOI | 10.5066/P9PRLNA3 |
Authors | Katherine J Knierim, James A Kingsbury, Katherine M Ransom |
Product Type | Data Release |
Record Source | USGS Asset Identifier Service (AIS) |
USGS Organization | Lower Mississippi-Gulf Water Science Center - Nashville, TN Office |
Rights | This work is marked with CC0 1.0 Universal |
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Manganese (Mn) concentrations and the probability of arsenic (As) exceeding the drinking-water standard of 10 μg/L were predicted in the Mississippi River Valley alluvial aquifer (MRVA) using boosted regression trees (BRT). BRT, a type of ensemble-tree machine-learning model, were created using predictor variables that affect Mn and As distribution in groundwater. These variables...
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