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

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...