Machine learning predictions of mean ages of shallow well samples in the Great Lakes Basin, USA
Machine learning predictions of mean ages of shallow well samples in the Great Lakes Basin, USA
复制标题
美国五大湖盆地浅井样本平均年龄的机器学习预测
DOI：
10.1016/j.jhydrol.2021.126908
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发表时间：
2021
影响因子：
6.4
通讯作者：
T. Harter
中科院分区：
文献类型：
--
作者：
C. T. Green;K. Ransom;B. Nolan;L. Liao;T. Harter
地下水的流动时间或“年龄”影响着集水区对水文变化、地球化学反应的响应，以及管理行动与下梯度溪流和井的响应之间的时间滞后。大气示踪剂的使用有助于表征地下水年龄，但大多数井缺乏这种测量。该研究利用机器学习技术，利用井、化学和景观特征，预测了五大湖盆地周围大片地区油井的年龄。对于961个样本的年龄示踪剂数据集，使用参数函数估计了每个样本从陆地表面到样本位置的旅行时间。然后使用梯度增强机（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方法能够密切匹配训练数据的估计年龄（RMSE=0.26自然对数尺度年），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（如深度）、土地利用、水文指标（如地形湿度指数）和水化学（如硝酸盐、氟化物和pH值）对年龄的预测有很大影响。将GBM预测应用于14335个地下水样本，样本中位数深度为5.4 m，表明大湖区水井年龄分布广泛，中位数为32.9年，表明水井对地表附近溶质通量变化的响应可能滞后数十年。虽然深度变量对预测的平均年龄影响最大，但化学成分随年龄的变化呈现平滑趋势，这与流行的演化源和地球化学流动路径概念模型一致。结果为利用井、景观和化学特征等现成变量来改善大区域地下水年龄估算提供了概念证明。
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 boosted machine (GBM) algorithm with cross validation tuning of model meta-parameters. 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, indicating that lag times of decades are likely for 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.