Mapped Predictions of Manganese and Arsenic in an Alluvial Aquifer Using Boosted Regression Trees

Abstract

Manganese (Mn) concentrations and the probability of arsenic (As) exceeding the drinking-water standard of 10 μg/L were predicted in the Mississippi River Valley alluvial aquifer (MRVA) using boosted regression trees (BRT). BRT, a type of ensemble-tree machine-learning model, were created using predictor variables that affect Mn and As distribution in groundwater. These variables included iron (Fe) concentrations and specific conductance predicted from previously developed BRT models, groundwater flux and age estimates from MODFLOW, and hydrologic characteristics. The models also included results from the first airborne geophysical survey conducted in the United States to target an entire aquifer system. Predictions of high Mn and As occurred where Fe was high. Predicted high Mn concentrations were correlated with fraction of young groundwater (less than 65 years) computed from MODFLOW results. High probabilities of As exceedance were predicted where groundwater was relatively old and airborne electromagnetic resistivity was high, typically proximal to streams. Two-variable partial-dependence plots and sensitivity analysis were used to provide insight into the factors controlling Mn and As distribution in groundwater. The maps of predicted Mn concentrations and As exceedance probabilities can be used to identify areas where these constituents may be high, and that could be targeted for further study. This paper shows that incorporation of a selected set of process-informed data, such as MODFLOW results and airborne geophysics, into a machine-learning model improves model interpretability. Incorporation of process-rich information into machine-learning models will likely be useful for addressing a wide range of problems of interest to groundwater hydrologists.

Predicting Redox Conditions in Groundwater at a National Scale Using Random Forest Classification

“…Conversely, discharge areas are more likely to be suboxic and the model correctly predicts that suboxic conditions are likely in the Mississippi Embayment , and in the trough of the Central Valley of California . Similarly, the high probability of Mn concentrations ≥50 μg/L in the Mississippi Embayment (Figure ) is consistent with previous work …”

“… 110 Similarly, the high probability of Mn concentrations ≥50 μg/L in the Mississippi Embayment ( Figure 7 ) is consistent with previous work. 111 …”

“…110 Similarly, the high probability of Mn concentrations ≥50 μg/L in the Mississippi Embayment (Figure 7) is consistent with previous work. 111 Predicted probabilities of oxic conditions should prove useful for assessing the vulnerability of groundwater to redoxsensitive contaminants. Redox-sensitive contaminants, such as nitrate and arsenic, are among the most common constituents that exceed maximum contaminant levels (MCLs) in public drinking water supplies.…”

Estimating Lithium Concentrations in Groundwater Used as Drinking Water for the Conterminous United States

“…Machine learning (ML) methods are becoming more widely utilized in the field of environmental science and can identify patterns in data not easily accomplished with traditional statistical methods . Several different ML approaches have been applied to predicting groundwater quality at regional and national scales including random forest and boosted regression trees. − Recently, extreme gradient boosting (XGB) models have been developed to predict manganese concentrations in the North Atlantic Coastal Plain aquifer system and nitrate concentrations in groundwater across the CONUS . The nitrate study found that the XGB model outperformed a boosted regression tree model based on the root-mean-square error from the cross-validation folds during model tuning.…”

Prioritizing water availability study settings to address geogenic contaminants and related societal factors

“…Constituent concentrations in upper-crustal materials are commonly sufficient that dissolution of small fractions during water–rock interaction can cause an exceedance of concentration thresholds for intended water use (Table S1 ). Concentrations of many geogenic constituents increase with groundwater age and flowpath length but also vary with aquifer lithology and hydrogeochemical conditions including pH and redox (DeSimone & Ransom, 2021 ; Erickson et al 2021a , 2021b ; Knierim et al, 2022 ; Lindsey et al, 2021 ; Stackelberg et al, 2021 ).…”