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James A Kingsbury

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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...
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Groundwater is a vital resource to the Mississippi embayment region of the central United States. Regional and integrated assessments of water availability that link physical flow models and water quality in principal aquifer systems provide context for the long-term availability of these water resources. An innovative approach using machine learning was employed to predict groundwater pH across drinking water aquifers of the Mississippi embayment. The region includes two principal regional aquifer systems; the Mississippi River Valley alluvial (MRVA) aquifer and the Mississippi embayment aquifer system that includes several regional aquifers and confining units. Based on the distribution of groundwater use for...
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Groundwater-quality data were collected from 502 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Project and are included in this data release. Most of the wells (500) were sampled from January through December 2015 and 2 of them were sampled in 2013. The data were collected from five types of well networks: principal aquifer study networks, which are used to assess the quality of groundwater used for public water supply; land-use study networks, which are used to assess land-use effects on shallow groundwater quality; major aquifer study networks, which are used to assess the quality of groundwater used for domestic supply; enhanced trends networks,...
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Machine-learning model predictions and groundwater-quality rasters of specific conductance, total dissolved solids, and chloride in aquifers of the Mississippi embayment
Groundwater is a vital resource in the Mississippi embayment of the central United States. An innovative approach using machine learning (ML) was employed to predict groundwater salinity—including specific conductance (SC), total dissolved solids (TDS), and chloride (Cl) concentrations—across three drinking-water aquifers of the Mississippi embayment. A ML approach was used because it accommodates a large and diverse set of explanatory variables, does not assume monotonic relations between predictors and response data, and results can be extrapolated to areas of the aquifer not sampled. These aspects of ML allowed potential drivers and sources of high salinity water that have been hypothesized in other studies to...
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This data release presents tabular data and water-level drawdown model files for 32 Mississippi River Valley alluvial aquifer monitoring wells and 4 Memphis aquifer observation wells from an aquifer test conducted in October 2017 at the Tennessee Valley Authority Allen power plants in Memphis, Shelby County, Tennessee. The dataset contains the water-level model files used to estimate drawdown in the monitoring and observation wells during the aquifer test, created using the SeriesSEE Excel add-in program (Halford and others, 2012). The SeriesSEE Excel add-in also is included so that water-level models can be reactivated. Reference Cited: Halford, K.J., Garcia, C.A., Fenlon, J.M., and Mirus, B.B., 2012, Advanced...
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