Item talk:Q274163
From geokb
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"name": "Predicting regional fluoride concentrations at public and domestic supply depths in basin-fill aquifers of the western United States using a random forest model",
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"abstract": "A random forest regression (RFR) model was applied to over 12,000 wells with measured fluoride (F) concentrations in untreated groundwater to predict F concentrations at depths used for domestic and public supply in basin-fill aquifers of the western United States. The model relied on twenty-two regional-scale environmental and surficial predictor variables selected to represent factors known to control F concentrations in groundwater. The testing model fit R2\u00a0and RMSE were 0.52 and 0.78\u00a0mg/L. Comparisons of measured to predicted proportions of four F-concentrations categories (<0.7\u00a0mg/L, 0.7\u20132\u00a0mg/L, >2\u00a0mg/L \u2013 4\u00a0mg/L, and\u00a0>\u00a04\u00a0mg/L) indicate that the model performed well at making regional-scale predictions. Differences between measured and predicted proportions indicate underprediction of measured F at values by between 4 and 20\u00a0mg/L, representing less than 1% of the regional scale predicted values. These residuals most often map to geographic regions where local-scale processes including evaporative discharge in\u00a0closed basins\u00a0or intermittent streams concentrate fluoride in shallow groundwater. Despite this, the RFR model provides spatially continuous F predictions across the basin-fill aquifers where discrete samples are missing. Further, the predictions capture documented areas that exceed the F maximum contaminant level for drinking water of 4\u00a0mg/L and areas that are below the oral-health benchmark of 0.7\u00a0mg/L. These predictions can be used to estimate fluoride concentrations in unmonitored areas and to aid in identifying geographic areas that may require further investigation at localized scales.",
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