Groundwater resources are crucial to safe drinking supplies in sub‐Saharan Africa, and will be increasingly relied upon in a context of climate change. The need to better understand groundwater calls for innovative approaches to make the best out of the existing information. A methodology to map groundwater potential based on an ensemble of machine learning classifiers is presented. A large borehole database (n = 1848) was integrated into a Geographic Information Systems (GIS) environment and used to train, validate and test 12 machine learning algorithms. Each classifier predicts a binary target (positive or negative borehole) based on the minimum flow rate required for communal domestic supplies. Classification is based on a number of explanatory variables, including landforms, lineaments, soil, vegetation, geology and slope, among others. Correlations between the target and explanatory variables were then generalized to develop groundwater potential maps. Most algorithms attained success rates between 80% and 96% in terms of test score, which suggests that the outcomes provide an accurate picture of field conditions. Statistical learners were observed to perform better than most other algorithms, excepting random forests and support vector machines. Furthermore, it is concluded that the ensemble approach provides added value by incorporating a measure of uncertainty to the results. This technique may be used to rapidly map groundwater potential for rural supply or humanitarian emergencies in areas where there is sufficient historical data but where comprehensive field work is unfeasible.

中文翻译：

---

通过一系列大数据方法绘制地下水潜力

地下水资源对于撒哈拉以南非洲地区的安全饮用水供应至关重要，在气候变化的背景下，地下水资源将越来越多地被依赖。为了更好地了解地下水，需要采用创新的方法来充分利用现有信息。提出了一种基于机器学习分类器的地图来绘制地下水潜力的方法。大型钻孔数据库（n = 1848年）集成到地理信息系统（GIS）环境中，并用于训练，验证和测试12种机器学习算法。每个分类器都基于公共家庭供应所需的最小流速来预测二进制目标（正井眼或负井眼）。分类基于许多解释性变量，包括地貌，界线，土壤，植被，地质和坡度等。然后，将目标变量与解释变量之间的相关性进行概括，以开发地下水潜力图。大多数算法在测试分数方面都获得了80％到96％的成功率，这表明结果可以提供对现场条件的准确描述。观察到统计学习者的表现比大多数其他算法要好，随机森林和支持向量机除外。此外，得出的结论是，集成方法通过将不确定性度量结合到结果中来提供附加值。在有足够的历史数据但无法进行全面的现场工作的地区，可以使用此技术快速绘制出地下水潜力以用于农村供水或人道主义紧急情况。