High salinity limits groundwater use in parts of the Mississippi embayment. Machine learning was used to create spatially continuous and three‐dimensional predictions of salinity across drinking‐water aquifers in the embayment. Boosted regression tree (BRT) models, a type of machine learning, were used to predict specific conductance (SC) and chloride (Cl), and total dissolved solids (TDS) was calculated from a correlation with SC. Explanatory variables for BRT models included well location and construction, surficial variables (e.g., soils and land use), and variables extracted from a groundwater‐flow model, including simulated groundwater ages. BRT model fits (r²) were 0.74 (SC and Cl) and 0.62 (TDS). BRT models provided spatially continuous salinity predictions across surficial and deeper aquifers where discrete water‐quality samples were missing. Uncertainty was smaller where salinity was lower, and models tended to underpredict in areas of highest salinity. Despite this, BRT models were able to capture areas of documented high salinity that exceed the TDS secondary maximum contaminant level for drinking water of 500 mg/L. Variables that served as surrogates for position along groundwater flowpaths were the most important predictors, indicating that much of the control on dissolved solids is related to rock‐water interaction as residence time increases. BRT models additionally support hypotheses of both surficial and deep sources of salinity.

中文翻译：

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使用增强回归树模型预测美国中部密西西比河沿岸含水层的盐度

高盐度限制了密西西比河隔离带部分地区的地下水使用。机器学习被用来创建空间中饮用水层中盐度的空间连续和三维预测。增强回归树（BRT）模型是一种机器学习方法，用于预测比电导（SC）和氯化物（Cl），并根据与SC的相关性计算总溶解固体（TDS）。BRT模型的解释变量包括井位和建设，表面变量（例如，土壤和土地利用）以及从地下水流模型提取的变量，包括模拟的地下水年龄。BRT模型拟合（r ²）分别为0.74（SC和Cl）和0.62（TDS）。BRT模型提供了缺少离散水质样本的表层和深层含水层的空间连续盐度预测。在盐度较低的地方，不确定性较小，在盐度最高的区域中，模型往往预测不足。尽管如此，BRT模型仍能够捕获记录在案的高盐度区域，这些区域的盐度超过了500 mg / L饮用水的TDS次要最大污染物水平。最重要的预测变量是作为沿地下水流路位置的替代变量，这表明随着停留时间的增加，对溶解固体的大部分控制与岩水相互作用有关。BRT模型还支持盐分的表层和深层来源的假设。