Urban water sources are susceptible to various contamination events as a result of natural, accidental, and human-induced occurrences. An early warning monitoring system provides timely information on changes in urban water quality. In this study, an analysis was made with CANARY event detection software (EDS) to monitor water quality parameters in river water and to identify the onset of anomalous water quality periods. Water quality signals including pH, conductivity, and turbidity from Milwaukee River over specified periods during the summer season of 2018-2020 were employed as inputs to event detection algorithms in CANARY. The data analysis results show that CANARY can be useful as an early warning system for monitoring contamination in urban water sources and help to identify abnormal conditions quickly. The sensibility of the model relies on optimizing the configuration parameters, which involves selecting the ideal set of parameters for the event detection algorithm and adjusting the BED parameters to increase or decrease the probability of generating an alarm. The number of events reported between the Linear Prediction Correction Filter (LPCF) and Multivariate Nearest Neighbor (MVNN) algorithms varied as a result of different residual calculation mechanisms. Climate factors that contributed to the abnormal water quality events in the river were examined. The analysis of rainfall on water quality was carried out using a statistical method by determining whether there is a significant difference (p-value) between the seasonal mean water quality data and the mean value of water parameters during the sampling duration. Regression analysis was also performed to estimate the best model that describes the relationship between each of the water quality parameters and temperature.

由于自然、意外和人为事件，城市水源容易受到各种污染事件的影响。预警监测系统可及时提供有关城市水质变化的信息。在这项研究中，使用 CANARY 事件检测软件 (EDS) 进行分析，以监测河水中的水质参数并识别异常水质时期的开始。2018-2020 年夏季特定时期密尔沃基河的水质信号，包括 pH 值、电导率和浊度，被用作 CANARY 事件检测算法的输入。数据分析结果表明，CANARY可作为监测城市水源污染的预警系统，有助于快速识别异常情况。该模型的敏感性依赖于优化配置参数，包括为事件检测算法选择理想的参数集并调整 BED 参数以增加或减少生成警报的概率。线性预测校正滤波器 (LPCF) 和多元最近邻 (MVNN) 算法之间报告的事件数量因残差计算机制不同而有所不同。检查了导致河流水质异常事件的气候因素。降雨对水质的分析是使用统计方法通过确定季节性平均水质数据与采样期间水参数平均值之间是否存在显着差异（p 值）来进行的。