Filters: partyWithName: U.S. Geological Survey (X) > Types: OGC WMS Service (X) > partyWithName: Bernard T Nolan (X)
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Widespread nitrate contamination of groundwater in agricultural areas poses a major challenge to sustainable water resources. Efficient analysis of nitrate fluxes across large regions also remains difficult. This study introduces a method of characterizing nitrate transport processes continuously across regional unsaturated zones and groundwater based on surrogate, machine-learning metamodels of an N flux process-based model. The metamodels used boosted regression trees (BRTs) to relate mappable variables to parameters and outputs of a “vertical flux method” (VFM) applied in the Fox-Wolf-Peshtigo (FWP) area in Wisconsin. In this context, the metamodels are upscaling the VFM results throughout the region, and the...
The ascii grids associated with this data release are model inputs representing the Central Valley aquifer, California, and predicted nitrate concentrations (as NO3-N, mg/L) at two depth zones associated with private and public drinking water supply wells, respectively. The model input and prediction grids are bound by the alluvial bed boundary that defines the Central Valley. The prediction grids were produced with Boosted Regression Tree (BRT) modeling methods within a statistical modeling framework using the statistical modeling software R (R Core Team, https://www.r-project.org/) and linear interpolation within Oasis Montaj software (Geosoft, version 9.0.2). The response variable was a set of nitrate concentrations...
The ascii grids represent regional probabilities that groundwater in a particular location will have dissolved oxygen (DO) concentrations less than selected threshold values representing anoxic groundwater conditions or will have dissolved manganese (Mn) concentrations greater than selected threshold values representing secondary drinking water-quality contaminant levels (SMCL) and health-based screening levels (HBSL) for water quality. The probability models were constrained by the alluvial boundary of the Central Valley to a depth of approximately 300 meters (m). We utilized prediction modeling methods, specifically boosted regression trees (BRT) with a Bernoulli error distribution within a statistical learning...
Categories: Publication;
Types: Citation,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Boosted Regression Trees,
California,
Central Valley, California,
Domestic Well Water Use,
Drinking Water Use,
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