Effective monitoring of groundwater contamination is crucial to protect human livelihoods and ecosystems. This paper presents a machine learning-based approach to improve groundwater monitoring networks by providing predictions of groundwater contamination in space. The method is demonstrated through a practical application in Central Spain, where nitrate was used as a proxy for groundwater contamination. Predictive mapping identifies the spatial markers for groundwater contamination based on twenty-four predictor variables and a dataset of 213 existing monitoring boreholes. Tree-based algorithms found meaningful associations between the explanatory variables and known nitrate concentrations. Comparing the outcomes of the algorithms with the areas officially delineated as vulnerable to nitrate suggests that machine learning algorithms are able to predict groundwater contamination. The extra trees algorithm outperformed decision trees, random forest, gradient boosting, and AdaBoost classifiers, with an area under the curve score in excess of 0.88. Major predictors for groundwater contamination were depth to the water table, lithology, distance to rivers, and distance to livestock farms. Predictive mapping suggests that there are unmonitored regions to the northeast and to the southwest of Madrid’s metropolitan area that present similar markers to monitored regions known to be contaminated. These unmonitored areas should be prioritized in future attempts to improve the network. From a research perspective, the main conclusion of this work is that machine learning techniques can be used as a technique to automate the siting of monitoring boreholes. Practical applications should nevertheless be overseen by an expert eye to guarantee the quality of the outcomes.

有效监测地下水污染对于保护人类生计和生态系统至关重要。本文提出了一种基于机器学习的方法，通过预测太空中的地下水污染来改进地下水监测网络。该方法在西班牙中部的实际应用中得到了证明，那里的硝酸盐被用作地下水污染的代表。预测性绘图根据 24 个预测变量和 213 个现有监测钻孔的数据集确定地下水污染的空间标记。基于树的算法在解释变量和已知的硝酸盐浓度之间发现了有意义的关联。将算法的结果与官方划定为易受硝酸盐影响的区域进行比较，表明机器学习算法能够预测地下水污染。额外树算法的性能优于决策树、随机森林、梯度提升和 AdaBoost 分类器，曲线下面积得分超过 0.88。地下水污染的主要预测因素是地下水位深度、岩性、到河流的距离和到畜牧场的距离。预测性绘图表明，马德里大都会区的东北部和西南部存在未监测的区域，这些区域与已知受污染的监测区域具有相似的标志。在未来尝试改善网络时，应优先考虑这些未受监控的区域。从研究的角度来看，这项工作的主要结论是机器学习技术可以用作自动定位监测钻孔的技术。然而，实际应用应由专家监督，以保证结果的质量。