Effective monitoring of groundwater withdrawals is necessary to help mitigate the negative impacts of aquifer depletion. In this study, we develop a holistic approach that combines water balance components with a machine learning model to estimate groundwater withdrawals. We use both multitemporal satellite and modeled data from sensors that measure different components of the water balance and land use at varying spatial and temporal resolutions. These remote sensing products include evapotranspiration, precipitation, and land cover. Due to the inherent complexity of integrating these data sets and subsequently relating them to groundwater withdrawals using physical models, we apply random forests—a state of the art machine learning algorithm—to overcome such limitations. Here, we predict groundwater withdrawals per unit area over a highly monitored portion of the High Plains aquifer in the central United States at 5 km resolution for the Years 2002–2019. Our modeled withdrawals had high accuracy on both training and testing data sets (R² ≈ 0.99 and R² ≈ 0.93, respectively) during leave‐one‐out (year) cross validation with low mean absolute error (MAE) ≈ 4.31 mm and root‐mean‐square error (RMSE) ≈ 13.50 mm for the year 2014. Moreover, we found that even for the extreme drought year of 2012, we have a satisfactory test score (R² ≈ 0.84) with MAE ≈ 9.72 mm and RMSE ≈ 24.17 mm. Therefore, the proposed machine learning approach should be applicable to similar regions for proactive water management practices.

中文翻译：

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集成多时相遥感数据集和机器学习的地下水提取预测

必须有效监测地下水抽取量，以帮助减轻含水层枯竭的负面影响。在这项研究中，我们开发了一种将水平衡要素与机器学习模型相结合的整体方法，以估算地下水抽取量。我们同时使用多时卫星和来自传感器的建模数据，这些传感器以不同的时空分辨率测量水平衡和土地利用的不同组成部分。这些遥感产品包括蒸散，降水和土地覆盖。由于整合这些数据集并随后使用物理模型将其与地下水抽取相关的内在复杂性，我们应用了随机森林（一种先进的机器学习算法）来克服此类限制。这里，我们预测2002-2019年美国中部高平原含水层中受高度监控的部分每平方公里的地下水抽取量为5 km。我们建模的提款在训练和测试数据集上均具有很高的准确性（[R ²  ≈0.99和- [R ²  ≈0.93，分别地）在留一出（年）配有低的平均绝对误差（MAE交叉验证）≈4.31毫米和根均方误差（RMSE）≈13.50毫米全年2014而且，我们发现，即使为2012的极端干旱年，我们有一个令人满意的测试得分（[R ²  ≈0.84）与MAE≈9.72毫米和RMSE≈24.17毫米。因此，建议的机器学习方法应适用于类似地区的主动水管理实践。