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A Machine Learning Approach to Estimate Domestic Use of Public and Private Water Sources in the United States.
Water Research ( IF 11.4 ) Pub Date : 2025-01-26 , DOI: 10.1016/j.watres.2025.123171
Andrew Murray, Alexander Hall, Diego Riveros-Iregui
Water Research ( IF 11.4 ) Pub Date : 2025-01-26 , DOI: 10.1016/j.watres.2025.123171
Andrew Murray, Alexander Hall, Diego Riveros-Iregui
In the United States, people obtain water for household use from one of two sources. Public water systems, which are subject to rules and regulations under the Safe Drinking Water Act, or private sources such as domestic wells, which are not subject to federal regulation and are generally the responsibility of the homeowner or occupant. Public water systems are required to treat their drinking water and conduct regular testing to ensure the delivery of safe water to consumers. From a public health perspective, it is essential to know who is drinking what water to determine risk and impacts from water-borne disease and contamination. We present a new machine-learning approach to estimating water supply source (public or private) at the census block level for the year 2020. While previous studies have largely focused on spatially delineating either public or private water supply, our method incorporates data from both universes, resulting in more accurate modeling results. The utilization of machine learning and additional explanatory data that have not been considered in prior studies results in the most accurate and up-to-date estimate of the count and location of users supplying household water from either a private source or a public water supply. We estimate that 14.1% of US housing units are supplied by private wells and 84.9% of housing units are served by a public water system as of 2020.
中文翻译:
一种机器学习方法,用于估计美国公共和私人水源的国内使用情况。
在美国,人们从两个来源之一获得家庭用水。公共供水系统(受《安全饮用水法》规定的规则和条例的约束)或私人水源(如家用水井)不受联邦法规的约束,通常由房主或居住者负责。公共供水系统必须处理其饮用水并进行定期检测,以确保向消费者提供安全的水。从公共卫生的角度来看,必须知道谁在喝什么水,以确定水传播疾病和污染的风险和影响。我们提出了一种新的机器学习方法来估计 2020 年人口普查区块级别的供水来源(公共或私人)。虽然以前的研究主要集中在空间上划定公共或私人供水,但我们的方法结合了来自两个领域的数据,从而产生了更准确的建模结果。利用机器学习和其他解释性数据,这些数据在以前的研究中没有被考虑过,从而对从私人水源或公共供水系统供应家庭用水的用户的数量和位置进行最准确和最新的估计。我们估计,截至 2020 年,美国 14.1% 的住房单元由私人水井供应,84.9% 的住房单元由公共供水系统提供。
更新日期:2025-01-27
中文翻译:
一种机器学习方法,用于估计美国公共和私人水源的国内使用情况。
在美国,人们从两个来源之一获得家庭用水。公共供水系统(受《安全饮用水法》规定的规则和条例的约束)或私人水源(如家用水井)不受联邦法规的约束,通常由房主或居住者负责。公共供水系统必须处理其饮用水并进行定期检测,以确保向消费者提供安全的水。从公共卫生的角度来看,必须知道谁在喝什么水,以确定水传播疾病和污染的风险和影响。我们提出了一种新的机器学习方法来估计 2020 年人口普查区块级别的供水来源(公共或私人)。虽然以前的研究主要集中在空间上划定公共或私人供水,但我们的方法结合了来自两个领域的数据,从而产生了更准确的建模结果。利用机器学习和其他解释性数据,这些数据在以前的研究中没有被考虑过,从而对从私人水源或公共供水系统供应家庭用水的用户的数量和位置进行最准确和最新的估计。我们估计,截至 2020 年,美国 14.1% 的住房单元由私人水井供应,84.9% 的住房单元由公共供水系统提供。