Machine-learning model predictions and rasters of specific conductance in the Mississippi Alluvial Plain
Boosted regression trees (BRT), a type of ensemble-tree machine-learning method, were used to predict specific conductance concentration at multiple depths throughout the Mississippi River Valley alluvial aquifer (MRVA) and underlying aquifers. Groundwater from the MRVA, coincident with the Mississippi Alluvial Plain (MAP), 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 salinity, measured as specific conductance. Two models were created to test the incorporation of datasets from a regional aerial electromagnetic (AEM) survey and evaluate model performance. Explanatory variables for the BRT models included attributes associated with well location and construction, surficial variables (such as hydrologic position and recharge), and variables from the AEM 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:
Killian, C.D. and Knierim, K.J., (2022) Machine learning predictions of groundwater specific conductance in the Mississippi Alluvial Plain, United States with evaluation of geophysical and regional aerial electromagnetic data as predictor variables, US Geological Survey Scientific Investigations Report XXXX.
Complete Metadata
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| description | Boosted regression trees (BRT), a type of ensemble-tree machine-learning method, were used to predict specific conductance concentration at multiple depths throughout the Mississippi River Valley alluvial aquifer (MRVA) and underlying aquifers. Groundwater from the MRVA, coincident with the Mississippi Alluvial Plain (MAP), 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 salinity, measured as specific conductance. Two models were created to test the incorporation of datasets from a regional aerial electromagnetic (AEM) survey and evaluate model performance. Explanatory variables for the BRT models included attributes associated with well location and construction, surficial variables (such as hydrologic position and recharge), and variables from the AEM 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: Killian, C.D. and Knierim, K.J., (2022) Machine learning predictions of groundwater specific conductance in the Mississippi Alluvial Plain, United States with evaluation of geophysical and regional aerial electromagnetic data as predictor variables, US Geological Survey Scientific Investigations Report XXXX. |
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| modified | 2023-11-29T00:00:00Z |
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| title | Machine-learning model predictions and rasters of specific conductance in the Mississippi Alluvial Plain |
