Recreational water quality is currently monitored at Sandpoint Beach on Lake St. Clair using culture-based enumeration of Escherichia coli. Using water quality and weather data collected over 4 yr, several multiple linear regression (MLR)-based models were developed for near real-time prediction of E. coli concentration and were tested using independent data from the fifth year. Model performance was assessed by the determination of metrics such as RMSE, accuracy, specificity, sensitivity, and area under the receiver operating characteristic curve (AUROC). Each of the developed MLR models described herein resulted in increased correct responses for both exceedance and non-exceedance of the applicable standard as compared to predictions based on E. coli measurements (persistence models, using the previous day's E. coli concentration), which is the method currently being used. The AUROC values for persistence models are between 0.5 and 0.6, as compared to >0.7 for all the MLR models described herein. Among the MLR models, model performance improved when qualitative sky weather condition, which is commonly reported but was not previously used in similar models, was included. To select the best model, a principal coordinate analysis was used to combine multiple model performance metrics and provide a more sensitive tool for model comparison. Although models developed using 2, 3, and 4 yr of monitoring data provided reasonable performance, the model developed using the most recent 2-yr data was marginally better. Thus, data from the most recent 2 yr are likely sufficient as a training dataset for updating the MLR model for Sandpoint Beach in the future.

中文翻译：

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评估大湖区圣克莱尔湖淡水海滩海滩管理的多个预测模型

目前在圣克莱尔湖的 Sandpoint 海滩使用基于培养的大肠杆菌计数来监测娱乐水质。使用 4 年多收集的水质和天气数据，开发了几个基于多元线性回归 (MLR) 的模型，用于近实时预测大肠杆菌浓度，并使用第 5 年的独立数据进行测试。模型性能通过确定诸如 RMSE、准确性、特异性、灵敏度和接受者操作特征曲线下面积 (AUROC) 等指标来评估。与基于大肠杆菌测量（持久性模型，使用前一天的大肠杆菌浓度）的预测相比，本文描述的每个开发的 MLR 模型都导致对超出和不超出适用标准的正确响应增加，这是目前使用的方法。与本文描述的所有 MLR 模型的 >0.7 相比，持久性模型的 AUROC 值介于 0.5 和 0.6 之间。在 MLR 模型中，当包含定性天空天气条件（通常报告但以前未在类似模型中使用）时，模型性能得到提高。为了选择最佳模型，使用主坐标分析来组合多个模型性能指标，并为模型比较提供更灵敏的工具。尽管使用 2、3 和 4 年的监测数据开发的模型提供了合理的性能，但使用最新的 2 年数据开发的模型略好一些。因此，最近 2 年的数据可能足以作为未来更新 Sandpoint Beach 的 MLR 模型的训练数据集。