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Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification

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Tesoriero, A. J., J. A. Gronberg, P. F. Juckem, M. P. Miller, and B. P. Austin (2017), Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification, Water Resour. Res., 53, doi:10.1002/2016WR020197.

Summary

Machine learning techniques were applied to a large (n > 10,000) compliance monitoring database to predict the occurrence of several redox-active constituents in groundwater across a large watershed. Specifically, random forest classification was used to determine the probabilities of detecting elevated concentrations of nitrate, iron, and arsenic in the Fox, Wolf, Peshtigo, and surrounding watersheds in northeastern Wisconsin. Random forest classification is well suited to describe the nonlinear relationships observed among several explanatory variables and the predicted probabilities of elevated concentrations of nitrate, iron, and arsenic. Maps of the probability of elevated nitrate, iron, and arsenic can be used to assess groundwater [...]

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Type Scheme Key
local-index unknown 70190391
local-pk unknown 70190391
doi http://www.loc.gov/standards/mods/mods-outline-3-5.html#identifier doi:10.1002/2016WR020197
series unknown Water Resources Research

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citationTypeArticle
journalWater Resources Research
languageEnglish
parts
typevolume
value53
typeissue
value8

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