Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona, 18-26, Barcelona 08026, Spain.
Sci Total Environ. 2012 Aug 15;432:365-74. doi: 10.1016/j.scitotenv.2012.05.097. Epub 2012 Jul 1.
The complex behavior observed for the dependence of trihalomethane formation on forty one water treatment plant (WTP) operational variables is investigated by means of linear and non-linear regression methods, including kernel-partial least squares (K-PLS), and support vector machine regression (SVR). Lower prediction errors of total trihalomethane concentrations (lower than 14% for external validation samples) were obtained when these two methods were applied in comparison to when linear regression methods were applied. A new visualization technique revealed the complex nonlinear relationships among the operational variables and displayed the existing correlations between input variables and the kernel matrix on one side and the support vectors on the other side. Whereas some water treatment plant variables like river water TOC and chloride concentrations, and breakpoint chlorination were not considered to be significant due to the multi-collinear effect in straight linear regression modeling methods, they were now confirmed to be significant using K-PLS and SVR non-linear modeling regression methods, proving the better performance of these methods for the prediction of complex formation of trihalomethanes in water disinfection plants.
通过线性和非线性回归方法，包括核偏最小二乘法 (K-PLS) 和支持向量机回归 (SVR)，研究了四十一座水处理厂 (WTP) 操作变量对三卤甲烷生成的依赖的复杂行为。与线性回归方法相比，当应用这两种方法时，总三卤甲烷浓度的预测误差较低（外部验证样本的误差低于 14%）。一种新的可视化技术揭示了操作变量之间复杂的非线性关系，并显示了输入变量与核矩阵以及支持向量之间的现有相关性。虽然一些水处理厂变量，如河水 TOC 和氯化物浓度以及断点氯化，由于直线线性回归建模方法中的多重共线性效应，被认为不显著，但现在使用 K-PLS 和 SVR 非线性建模回归方法确认它们是显著的，证明了这些方法在预测水消毒厂中三卤甲烷的复杂形成方面具有更好的性能。
