Observed, predicted, and  misclassification error data for observations in the training datset for nitrate and arsenic concentrations in basin-fill aquifers in the Southwest Principal Aquifers study.

This product "Observed, predicted, and misclassification error data for observations in the 
training dataset for nitrate and arsenic   concentrations in basin-fill aquifers in the Southwest 
Principal Aquifers study" is a 1:250,000-scale point dataset and was developed as part of a 
regional Southwest Principal Aquifers (SWPA) study. The study examined the vulnerability 
of basin-fill aquifers in the southwestern United States to nitrate contamination and arsenic 
enrichment. Statistical models were developed by using the random forest classifier algorithm  
to predict concentrations of nitrate and arsenic across a model grid that represents local- and 
basin-scale measures of source, aquifer susceptibility, and geochemical conditions.
		
Separate classifiers   were developed for nitrate and arsenic because each constituent was 
expected to be affected by a different set of factors, and each factor could have a different 
magnitude or directional influence (increase/decrease) on concentration. For each constituent, 
two different classifiers were developed; a prediction classifier and a confirmatory classifier. 
The prediction classifiers were developed specifically to predict nitrate and arsenic 
concentrations in basin-fill aquifers across the SWPA study area and were based on 
explanatory variables representing source and susceptibility conditions. These explanatory 
variables were available throughout the entire SWPA study area and, therefore, did not pose 
a limitation for using the classifiers to predict concentrations.
		
The confirmatory classifiers were developed to supplement the prediction classifiers in the 
evaluation of the conceptual model. The name, "confirmatory," reflects the classifier's purpose 
for evaluation of a-priori hypotheses and contrasts other general types of statistical models, 
such as those used for prediction or exploratory purposes. The  confirmatory classifiers 
included the explanatory variables used in the prediction classifiers, as well as additional 
variables representing geochemical conditions and basin groundwater budget components. 
The inclusion of the geochemical and basin groundwater budget variables in the confirmatory 
classifiers allowed for further evaluation of the conceptual models, which was not possible 
with the prediction classifiers alone. The geochemical data, however, were only available at 
specific well locations, and consistent water-budget data were not available for every basin 
in the study area. The limited availability of the data for these variables constrained the 
confirmatory classifiers to observations from 16 case-study basins and precluded use of 
the confirmatory classifier for predicting concentrations across the SWPA study area. To 
contrast the scope of the two classifiers, the confirmatory classifiers were developed by 
using all available explanatory variables but with observations restricted to the 16 case-study 
basins, whereas the prediction classifiers were unrestricted with respect to spatial extent 
because these were developed by using a subset of the explanatory variables that were 
available throughout the study area.

Tags: arizona, arsenic-concentration, basin-fill-aquifer, california, colorado, environment, geoscientificinformation, groundwater, groundwater-contamination, groundwater-susceptibility, inlandwaters, national-water-quality-assessment-program, nawqa, nevada, new-mexico, nitrate-concentration, southwest-united-states, usgs-1d589b73-af80-4229-bd58-c62dd4192bc4, utah, utilitiescommunication, water-quality
Organization: Department of the Interior