Mapped Predictions of Manganese and Arsenic in an Alluvial Aquifer Using Boosted Regression Trees.

ABSTRACT: 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 included iron (Fe) concentrations and specific conductance predicted from previously developed BRT models, groundwater flux and age estimates from MODFLOW, and hydrologic characteristics. The models also included results from the first airborne geophysical survey conducted in the United States to target an entire aquifer system. Predictions of high Mn and As occurred where Fe was high. Predicted high Mn concentrat

SUBMITTER: Knierim KJ 

PROVIDER: S-EPMC9302655 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature