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Folders: ROOT > ScienceBase Catalog > National Water-Quality Assessment Project > Groundwater Studies > Supporting Data & Models > Statistical Models ( Show direct descendants )

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_ScienceBase Catalog
__National Water-Quality Assessment Project
___Groundwater Studies
____Supporting Data & Models
_____Statistical Models
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A boosted regression tree (BRT) model was developed to predict pH conditions in three-dimensions throughout the glacial aquifer system (GLAC) of the contiguous United States using pH measurements in samples from 18,258 wells and predictor variables that represent aspects of the hydrogeologic setting. Model results indicate that the carbonate content of soils and aquifer materials strongly controls pH and when coupled with long flow paths, results in the most alkaline conditions. Conversely, in areas where glacial sediments are thin and carbonate-poor, pH conditions remain acidic. At depths typical of drinking-water supplies, predicted pH > 7.5 – which is associated with arsenic mobilization – occurs more frequently...
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Ensemble-tree machine learning (ML) regression models can be prone to systematic bias: small values are overestimated and large values are underestimated. Additional bias can be introduced if the dependent variable is a transform of the original data. Six methods were evaluated for their ability to correct systematic and introduced bias: (1) empirical distribution matching (EDM); (2) regression of observed on estimated values (ROE); (3) linear transfer function (LTF); (4) linear equation based on Z-score transform (ZZ); (5) second machine learning model used to estimate residuals (ML2-RES); and (6) Duan smearing estimate applied after ROE is implemented (ROE-Duan). The performance of the methods was evaluated using...
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This data release includes grids representing the depth and thickness of drinking-water withdrawal zones, polygons of hydrogeologic settings, an inventory of sources of well construction data, and summaries of data comparisons used to assess the depth of groundwater used for drinking-water supplies in the United States. Well construction data sources are documented in Table1_DataSources.xlsx. Data comparisons using the Mann-Whitney test to assess similarity between hydrogeologic settings were used to justify combining data where they were sparse (compare_neighbors_all_domestic.txt and compare_neighbors_all_public.txt). Water-supply-well depth varies geographically by water use and the type of well, which illustrates...


    map background search result map search result map Data for depth of groundwater used for drinking-water supplies in the United States Data for machine learning predictions of pH in the glacial aquifer system, northern USA Data Release for Evaluation of Six Methods for Correcting Bias in Estimates from Ensemble Tree Machine Learning Regression Models Data for machine learning predictions of pH in the glacial aquifer system, northern USA Data Release for Evaluation of Six Methods for Correcting Bias in Estimates from Ensemble Tree Machine Learning Regression Models Data for depth of groundwater used for drinking-water supplies in the United States