Original paper
Predicting redox‐sensitive contaminant concentrations in groundwater using random forest classification
Abstract
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...
Paper Details
Title
Predicting redox‐sensitive contaminant concentrations in groundwater using random forest classification
Published Date
Aug 1, 2017
Journal
Volume
53
Issue
8
Pages
7316 - 7331
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Notes
History