Altitude of the potentiometric surface in the Mississippi River Valley alluvial aquifer, spring 2018

McGuire, Virginia L.; Seanor, Ronald C.; Asquith, William H.; Nottmeier, Anna M.; Smith, David C.; Tollett, Roland W.; Kress, Wade H.; Strauch, Kellan R.

doi:10.3133/sim3453

Cited by 4 publications

(2 citation statements)

References 20 publications

(25 reference statements)

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“…Predictions were made using the final hyperparameters selected for each BRT model and a flat file of predictor variable values at each raster cell (Knierim et al 2021a). The depth represented by each raster cell (and hence depth of BRT model prediction) was the mid‐point depth of MRVA saturated thickness, calculated using the 2018 potentiometric groundwater altitude (McGuire et al 2020) and the bottom of the MRVA (Torak and Painter 2019a, 2019b). Wells used in model training may have different depths than the prediction depth of the coincident raster cell where the well is located, but BRT methods can handle this sort of complexity.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

Knierim¹,

Kingsbury

Belitz

et al. 2022

Groundwater

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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 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 concentrations were correlated with fraction of young groundwater (less than 65 years) computed from MODFLOW results. High probabilities of As exceedance were predicted where groundwater was relatively old and airborne electromagnetic resistivity was high, typically proximal to streams. Two-variable partial-dependence plots and sensitivity analysis were used to provide insight into the factors controlling Mn and As distribution in groundwater. The maps of predicted Mn concentrations and As exceedance probabilities can be used to identify areas where these constituents may be high, and that could be targeted for further study. This paper shows that incorporation of a selected set of process-informed data, such as MODFLOW results and airborne geophysics, into a machine-learning model improves model interpretability. Incorporation of process-rich information into machine-learning models will likely be useful for addressing a wide range of problems of interest to groundwater hydrologists.

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Section: Methodsmentioning

confidence: 99%

“…Location of the Mississippi River Valley alluvial aquifer and regions associated with the Mississippi Alluvial Plain. Depth to water from 2018 calculated from McGuire et al (2020). …”

Section: Introductionmentioning

confidence: 99%

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

Knierim¹,

Kingsbury

Belitz

et al. 2022

Groundwater

View full text Add to dashboard Cite

show abstract

Modelling the climate change and cotton yield relationship in Mississippi: Autoregressive distributed lag approach

Kumar Sharma,

Dhillon,

Kumar

et al. 2024

Ecological Indicators

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Machine-learning predictions of groundwater specific conductance in the Mississippi Alluvial Plain, south-central United States, with evaluation of regional geophysical aerial electromagnetic data as explanatory variables

Altitude of the potentiometric surface in the Mississippi River Valley alluvial aquifer, spring 2018

Cited by 4 publications

References 20 publications

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

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

Modelling the climate change and cotton yield relationship in Mississippi: Autoregressive distributed lag approach

Machine-learning predictions of groundwater specific conductance in the Mississippi Alluvial Plain, south-central United States, with evaluation of regional geophysical aerial electromagnetic data as explanatory variables

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