Monitoring access to safely managed drinking water services requires information on water quality. An increasing number of countries have integrated water quality testing in household surveys however it is not anticipated that such tests will be included in all future surveys. Using water testing data from the 2016 Ethiopia Socio-Economic Survey (ESS) we developed predictive models to identify households using contaminated (≥1 E. coli per 100 mL) drinking water sources based on common machine learning classification algorithms. These models were then applied to the 2013–2014 and 2018–2019 waves of the ESS that did not include water testing. The highest performing model achieved good accuracy (88.5%; 95% CI 86.3%, 90.6%) and discrimination (AUC 0.91; 95% CI 0.89, 0.94). The use of demographic, socioeconomic, and geospatial variables provided comparable results to that of the full features model whereas a model based exclusively on water source type performed poorly. Drinking water quality at the point of collection can be predicted from demographic, socioeconomic, and geospatial variables that are often available in household surveys.

监测安全管理的饮用水服务的获取需要水质信息。越来越多的国家将水质测试纳入家庭调查，但预计此类测试不会包含在未来的所有调查中。利用 2016 年埃塞俄比亚社会经济调查 (ESS) 的水测试数据，我们开发了预测模型来识别使用受污染（≥1大肠杆菌每 100 毫升）基于常见机器学习分类算法的饮用水源。然后将这些模型应用于 2013-2014 年和 2018-2019 年的 ESS 浪潮（不包括水测试）。性能最高的模型实现了良好的准确性（88.5%；95% CI 86.3%、90.6%）和辨别力（AUC 0.91；95% CI 0.89、0.94）。人口、社会经济和地理空间变量的使用提供了与完整特征模型相当的结果，而完全基于水源类型的模型表现不佳。收集点的饮用水质量可以根据家庭调查中经常提供的人口、社会经济和地理空间变量来预测。