The travel time or “age” of groundwater affects catchment responses to hydrologic changes, geochemical reactions, and time lags between management actions and responses at down-gradient streams and wells. Use of atmospheric tracers has facilitated the characterization of groundwater ages, but most wells lack such measurements. This study applied machine learning to predict ages in wells across a large region around the Great Lakes Basin using well, chemistry, and landscape characteristics. For a dataset of age tracers in 961 samples, the travel time from the land surface to the sample location was estimated for each sample using parametric functions. The mean travel times were then modeled using a gradient boosting machine (GBM) algorithm with cross validation tuning of model metaparameters. The GBM approach was able to closely match estimated ages for the training data (RMSE = 0.26 natural-log scale years) and provided a reasonable match to testing data (RMSE = 0.84). Of the variables tested, well characteristics (e.g. depth), land use, hydrologic indicators (e.g. topographic wetness index), and water chemistry (e.g. nitrate, fluoride, and pH), substantially affected the predictions of age. GBM prediction was applied to 14,335 groundwater samples with median sample depth of 5.4 m, indicating for the Great Lakes Basin a broad distribution of ages among wells with a median of 32.9 years. Lag times of decades are likely for these wells to respond to changing solute fluxes near land surface. While depth variables most strongly affected predicted mean ages, chemical constituents exhibited smooth trends with age, consistent with prevailing conceptual models of evolving sources and geochemistry flowpaths. The results provide proof of concept for use of readily available variables of well, landscape, and chemical characteristics to improve groundwater age estimates across large regions.

地下水的流动时间或“年龄”会影响集水区对水文变化、地球化学反应的响应，以及管理行动与下游河流和井的响应之间的时间滞后。大气示踪剂的使用促进了地下水年龄的表征，但大多数井缺乏这样的测量。本研究应用机器学习，利用井、化学和景观特征来预测五大湖盆地周围大片区域的井年龄。对于 961 个样本中的年龄示踪剂数据集，使用参数函数估计每个样本从地表到样本位置的旅行时间。然后使用梯度提升机 (GBM) 算法对平均旅行时间进行建模，并对模型元参数进行交叉验证调整。GBM 方法能够紧密匹配训练数据的估计年龄（RMSE = 0.26 自然对数年），并提供与测试数据的合理匹配（RMSE = 0.84）。在测试的变量中，井特征（例如深度）、土地利用、水文指标（例如地形湿度指数）和水化学（例如硝酸盐、氟化物和 pH 值）对年龄的预测产生了重大影响。GBM 预测应用于 14,335 个地下水样本，样本深度中位数为 5.4 m，表明五大湖盆地井间年龄分布广泛，中位数为 32.9 年。这些井可能会延迟几十年才能响应地表附近不断变化的溶质通量。虽然深度变量对预测的平均年龄影响最大，但化学成分随着年龄的增长表现出平滑的趋势，与演化源和地球化学流径的流行概念模型一致。结果提供了使用现成的井、景观和化学特征变量来改进大区域地下水年龄估计的概念证明。