Abstract：
Harmful algal blooms (HABs) or higher levels of de facto water reuse (DFR) can increase the levels of certain contaminants at drinking water intakes. Therefore, the goal of this study was to use multi-class supervised machine learning (SML) classification with data collected from six online instruments measuring fourteen total water quality parameters to detect cyanobacteria (corresponding to approximately 950 cells/mL, 2900 cells/mL, and 8600 cells/mL) or DFR (0.5, 1 and 2 % of wastewater effluent) events in the raw water entering an intake. Among 56 screened models from the caret package in R, four (mda, LogitBoost, bagFDAGCV, and xgbTree) were selected for optimization. mda had the greatest testing set accuracy, 98.09 %, after optimization with 7 false alerts. Some of the most important water parameters for the different models were phycocyanin-like fluorescence, UVA254, and pH. SML could detect algae blending events (estimated <9000 cells/mL) due in part to the phycocyanin-like fluorescence sensor. UVA254 helped identify higher concentrations of DFR. These results show that multi-class SML classification could be used at drinking water intakes in conjunction with online instrumentation to detect and differentiate HABs and DFR events. This could be used to create alert systems for the water utilities at the intake, rather than the finished water, so any adjustment to the treatment process could be implemented.
摘要：
有害藻华（HAB）或更高水平的事实上的水再利用（DFR）会增加饮用水入口处某些污染物的水平。因此,这项研究的目的是使用多类监督机器学习（SML）分类，从六个在线仪器收集的数据测量十四个总水质参数，以检测蓝藻（对应于大约950个细胞/mL，2900个细胞/mL，和8600个细胞/mL）或DFR（废水流出物为0.5、1和2％）进入取水口的原水中的事件。在R的插入符号包中的56个筛选模型中，四个(mda,LogitBoost,bagFDAGCV,和xgbTree)进行优化。mda具有最高的测试集准确性，98.09%,优化后有7个错误警报。不同模型的一些最重要的水参数是藻蓝蛋白样荧光，UVA254，和pH。SML可以检测藻类混合事件(估计<9000个细胞/mL),部分归因于藻蓝蛋白样荧光传感器。UVA254有助于识别更高浓度的DFR。这些结果表明，多类SML分类可用于饮用水取水口，并与在线仪器结合使用，以检测和区分HAB和DFR事件。这可以用来为进水口的供水设施创建警报系统，而不是成品水，因此可以对处理过程进行任何调整。
