Water-quality incidents in the water supply can occur because of errors in pressurization plant operations, leaks, and pollutant infiltration. They lead to citizen complaints about water quality. Identifying high-risk areas and contributing factors for accidents helps to prevent incidents. Here, a predictive analysis technique based on a geographic information system and complaint data was developed using SaTScan, a space–time statistical analysis program. High-risk clusters were identified using maximum log-likelihood ratios, relative risks, and Monte Carlo hypothesis testing. After a red water accident, high-risk clusters (C6–C9) were concentrated in one area. The relative risk of C7 before the accident was 4.58, double that of C2 (2.21). Using ArcGIS, a utility network dataset was created for the water-supply infrastructure in the high-risk cluster area. A utility network model enables flow simulation of the target water supply and tracing of the scope of influence. Downstream analysis of the trace showed that C8 had the greatest expected range of damage following an accident. To address this issue, four valves requiring controls were identified. C6 was predicted to suffer the most significant damage in an accident because it was closest to the water purification plant and had the largest pipe diameter.

中文翻译：

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使用时空扫描统计和公用设施网络建模检测供水系统的集群和跟踪管理

由于加压装置操作错误、泄漏和污染物渗透，可能会发生供水水质事故。它们导致公民对水质提出投诉。识别高风险区域和事故影响因素有助于预防事故发生。这里，使用时空统计分析程序 SaTScan 开发了基于地理信息系统和投诉数据的预测分析技术。使用最大对数似然比、相对风险和蒙特卡罗假设检验来识别高风险集群。红水事故发生后，高风险群（C6-C9）集中在一个地区。事故发生前C7的相对风险为4.58，是C2（2.21）的两倍。利用ArcGIS，为高风险集聚区的供水基础设施创建了公共设施网络数据集。公用网络模型可以对目标供水进行流量模拟并追踪影响范围。痕迹的下游分析表明，C8 在事故发生后具有最大的预期损坏范围。为了解决这个问题，确定了四个需要控制的阀门。 C6预计在事故中遭受最严重的损坏，因为它距离净水厂最近且管道直径最大。