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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...
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...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Boosted regression trees,
Calcite saturation index,
Connecticut,
Geochemistry,
Geochemistry,
An extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate in shallow groundwater across the conterminous United States (CONUS). Nitrate was predicted at a 1-square-kilometer (km) resolution at a depth below the water table of 10 m. The model builds off a previous XGB machine learning model developed to predict nitrate at domestic and public supply groundwater zones (Ransom and others, 2022) by incorporating additional monitoring well samples and modifying and adding predictor variables. The shallow zone model included variables representing well characteristics, hydrologic conditions, soil type, geology, climate, oxidation/reduction, and nitrogen inputs. Predictor...
Green and others (2021) developed a gradient boosted regression tree model to predict the mean groundwater age, or travel time, for shallow wells across a portion of the Great Lakes basin in the United States. Their study applied machine learning methods to predict ages in wells using well construction, well chemistry, and landscape characteristics. For a dataset of age tracers in 961 water samples, the mean travel time from the land surface to the sample location (center of saturated open interval) was estimated for each sample using parametric functions. The mean travel times were then modeled using a gradient boosting machine algorithm with cross validation tuning of model hyperparameters. The model contained...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: USGS Science Data Catalog (SDC),
Wisconsin,
aquifer system,
groundwater,
hydrogeology,
Data used to model and map manganese concentrations in groundwater in the Northern Atlantic Coastal Plain (NACP) aquifer system, eastern USA, are documented in this data release. The model predicts manganese concentration within four classes and is based on concentration data from 4492 wells. The well data were compiled from U.S. Geological Survey, U.S. Environmental Protection Agency, Suffolk County Water Authority (Suffolk County, New York), and state agency sources. The four concentration classes are based on guidelines for drinking water quality: below detection (class 1, less than 10 micrograms per liter (ug/L)); detected but less than the aesthetic guideline of 50 ug/L (class 2); greater than the aesthetic...
A random forest regression (RFR) model was developed to predict groundwater fluoride concentrations in four western United Stated principal aquifers —California Coastal basin-fill aquifers, Central Valley aquifer system, Basin and Range basin-fill aquifers, and the Rio Grande aquifer system. The selected basin-fill aquifers are a vital resource for drinking-water supplies. The RFR model was developed with a dataset of over 12,000 wells sampled for fluoride between 2000 and 2018. This data release provides rasters of predicted fluoride concentrations at depth typical of domestic and public supply wells in the selected basin-fill aquifers and includes the final RFR model that documents the prediction modeling process...
A three-dimensional extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate in groundwater across the conterminous United States (CONUS). Nitrate was predicted at a 1-square-kilometer (km) resolution for two drinking water zones, each of variable depth, one for domestic supply and one for public supply. The model used measured nitrate concentrations from 12,082 wells, and included predictor variables representing well characteristics, hydrologic conditions, soil type, geology, land use, climate, and nitrogen inputs. Predictor variables derived from empirical or numerical process-based models were also included to integrate information on controlling processes and...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Cycle 3,
Hydrology,
NAWQA,
USGS Science Data Catalog (SDC),
United States,
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