Department of Civil and Environmental Engineering, Henry Samueli School of Engineering, University of California, Irvine, CA 92697, USA.
Water Sci Technol. 2010;61(2):545-53. doi: 10.2166/wst.2010.842.
The study used existing indicator bacterial data and a number of physicochemical parameters that can be measured instantaneously to determine if a decision tree approach, especially classification and regression tree, could be used to predict bacterial concentrations in timely manner for beach closure management. Each indicator bacteria showed different tree structures and each had its own significant variables; Dissolved oxygen played an important role for both total coliform and fecal coliform and turbidity was the most important factor to predict concentrations of enterococci for decision tree approaches. Root mean squared error stayed between 5 and 6.5% of the average values of observations; RMSEs from each simulation, 0.25 for total coliform, 0.31 for fecal coliform, and 0.29 for enterococci. Estimations from tree structures would be regarded as a good representation of the actual data. In addition to results of the objective function, RMSE, 77.5% of actual value fell into the 95% of confidence interval of estimations for total coliform concentrations, 60% for fecal coliform concentrations, and 62.5% for enterococci concentrations. The approach showed reliable estimations for the majority of the data processed, although the method did not portray low concentrations of bacteria as well.
本研究使用现有的指示菌数据和一些可即时测量的理化参数，来确定决策树方法（特别是分类回归树）是否可用于及时预测海滩关闭管理中的细菌浓度。每种指示菌都表现出不同的树结构，并且都有其自身的显著变量；溶解氧对总大肠菌群和粪大肠菌群都有重要作用，浊度是预测肠球菌浓度的最重要因素。均方根误差（RMSE）保持在观测平均值的 5%到 6.5%之间；每次模拟的 RMSE 分别为总大肠菌群 0.25、粪大肠菌群 0.31 和肠球菌 0.29。树结构的估计值可以作为实际数据的良好代表。除了目标函数的结果外，实际值的 77.5%落入了总大肠菌群浓度估计值的 95%置信区间，粪大肠菌群浓度的 60%和肠球菌浓度的 62.5%。该方法对处理的大部分数据进行了可靠的估计，尽管该方法也无法很好地描绘低浓度的细菌。
