thumbnail

Digital spatial data for observed, predicted, and misclassification errors for observations in the training dataset for nitrate and arsenic concentrations in basin-fill aquifers in the Southwest Principal Aquifers study area

Data Series 697
National Water-Quality Assessment Program
By:  and 
https://doi.org/10.3133/ds697

Links

Abstract

This product "Digital spatial data for observed, predicted, and misclassification errors for observations in the training dataset for nitrate and arsenic concentrations in basin-fill aquifers in the Southwest Principal Aquifers study area" is a 1:250,000-scale point spatial dataset developed as part of a regional Southwest Principal Aquifers (SWPA) study (Anning and others, 2012). 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.

Study Area

Publication type Report
Publication Subtype USGS Numbered Series
Title Digital spatial data for observed, predicted, and misclassification errors for observations in the training dataset for nitrate and arsenic concentrations in basin-fill aquifers in the Southwest Principal Aquifers study area
Series title Data Series
Series number 697
DOI 10.3133/ds697
Year Published 2012
Language English
Publisher U.S. Geological Survey
Publisher location Reston, VA
Contributing office(s) Utah Water Science Center
Description Report: iv, 2 p.; Metadata
Country United States
State Arizona, California, Colorado, Idaho, Nevada, New Mexico, Oregon, Utah
Google Analytic Metrics Metrics page
Additional publication details