Large-scale data integration and harmonization to accurately predict sites facing future health-based drinking water crises

在美国，每年有多达4500万人直接受到基于健康的饮酒的影响 水的问题。这导致至少1600万例急性胃肠炎与污染直接相关， 社区水系统，还有数千万人直接受到化学和有机污染物的影响。 在缺水地区、服务不足的人口中， 在其他已经遭受健康差距的弱势群体中。这些水问题中有许多是 管理疏忽，不一致的监控以及缺乏预测能力的直接结果 接下来可能会出现问题。虽然饮用水问题的原因很复杂，但如果我们能够预测 如果将来出现基于健康的饮用水问题， 每年对数千万美国人产生积极影响。有趣的是，关于水的大量数据 市政供水系统的质量和性能已经存在于大型的、不同的数据库中。这些 数据库在很大程度上被忽视，即使使用，通常也只是偶尔和追溯性地使用。 初步证据表明，这些现有的数据库，其中载有历史的行政 违规和低于阈值的水质结果，可以挖掘，以准确预测未来的饮用水 危机上级统计研究研发团队是一个国际公认的水资源专家团队 在统计/数据分析/建模/计算、水质监测、 生物和化学污染物，并能够清楚地和令人信服地翻译水质， 健康信息转化为个人、组织和社区的可行步骤。在这个一期工程中， 我们将证明，利用已经收集的数据， 水质和市政供水系统性能的历史数据。我们将开始， 在两个不同的州（密歇根州和爱荷华州）不同的水质和市政供水系统性能。 然后，我们将利用机器学习技术来预测基于健康的违规历史，并将评估我们的 通过比较预测的违规行为与过去5年中基于健康的实际违规行为的方法。最后， 我们将确定至少10个由我们的算法确定为未来健康风险最高的城市， 基于水的问题，并将做系统的采样，以确认我们的模型为基础的预测。然后我们将 通过探索我们的基于模型的方法，展示如何利用这些预测来实现盈利 预测可以以经济、可用的形式呈现给客户。证明我们的理念和盈利能力 两个州的模型（第一阶段）将为我们建立广泛的（多州）数据库协调， 改进第二阶段拟议的机器学习/建模工作。具有多状态协调 数据集，识别特定州/地区的关键数据差距，以及经过验证的财务模型，我们的技术 将最终导致每年基于健康的饮用水问题数量的大幅减少。

Project summary: Up to 45 million people per year in the U.S. are directly impacted by health-based drinking water problems. This leads to at least 16 million cases of acute gastroenteritis directly linked to pollution at community water systems, with tens of millions more directly impacted by chemical and organic pollutants. Impacts are further exacerbated in locations dealing with water scarcity, in under-served populations, and within other vulnerable populations already suffering from health disparities. Many of these water problems are the direct result of managerial negligence, inconsistent monitoring, and a lack of the ability to anticipate where problems may arise next. While the reasons for drinking water problems are complex, if we could anticipate where health-based drinking water problems were to occur in the future, it could have an immediate and positive impact on tens of millions of Americans annually. Interestingly, extensive data about water quality and the performance of municipal water systems already exists in large, disparate databases. These databases are largely ignored and, when used, are typically used only anecdotally and retroactively. Preliminary evidence suggests that these existing databases, which contain histories of administrative violations and sub-threshold water-quality results, can be mined to accurately predict future drinking water crises. The Superior Statistical Research R&D team is an internationally recognized group of water experts with cross-cutting expertise in statistics/data analysis/modelling/computing, water-quality monitoring of biological and chemical contaminants, and the ability to clearly and compellingly translate water-quality and health information to actionable steps for individuals, organizations and communities. In this Phase I project, we will show that it is possible to predict water-related, health-based problem areas utilizing already collected, historical data on water quality and municipal water system performance. We will begin by harmonizing the disparate water quality and municipal water system performance in two different states (Michigan and Iowa). We will then utilize machine-learning techniques to predict health-based violation histories and will evaluate our methods by comparing predicted violations to actual health-based violations in the previous 5 years. Finally, we will identify at least 10 municipalities determined by our algorithm to be at the highest risk for future health- based water problems and will do systematic sampling to confirm our model-based predictions. We will then demonstrate how making these predictions can be leveraged to profitability by exploring how our model-based predictions can be presented to customers in an economical, usable form. Proof of our concept and profitability models in two states (Phase I) will set us up for widespread (multi-state) database harmonization and improvement of the proposed machine-learning/modelling effort in Phase II. With multi-state harmonized datasets, identification of key data gaps in particular states/areas, and proven financial models, our technology will ultimately lead to dramatic reductions in the number of health-based drinking water problems annually.