Université de Lyon, INSA Lyon, LGCIE, 34 Avenue des Arts, F-69621 Villeurbanne Cedex, France.
Water Sci Technol. 2010;62(4):875-82. doi: 10.2166/wst.2010.324.
Regression models are among the most frequently used models to estimate pollutants event mean concentrations (EMC) in wet weather discharges in urban catchments. Two main questions dealing with the calibration of EMC regression models are investigated: i) the sensitivity of models to the size and the content of data sets used for their calibration, ii) the change of modelling results when models are re-calibrated when data sets grow and change with time when new experimental data are collected. Based on an experimental data set of 64 rain events monitored in a densely urbanised catchment, four TSS EMC regression models (two log-linear and two linear models) with two or three explanatory variables have been derived and analysed. Model calibration with the iterative re-weighted least squares method is less sensitive and leads to more robust results than the ordinary least squares method. Three calibration options have been investigated: two options accounting for the chronological order of the observations, one option using random samples of events from the whole available data set. Results obtained with the best performing non linear model clearly indicate that the model is highly sensitive to the size and the content of the data set used for its calibration.
回归模型是估算城市流域中雨水排放污染物事件平均浓度（EMC）时最常用的模型之一。本文研究了校准 EMC 回归模型时需要处理的两个主要问题：i）模型对用于校准的数据集的大小和内容的敏感性，ii）当数据集随时间增长和变化且新的实验数据被收集时，模型重新校准时的建模结果的变化。基于在一个高度城市化流域中监测到的 64 次降雨事件的实验数据集，已经推导出并分析了四个 TSS EMC 回归模型（两个对数线性模型和两个线性模型），它们具有两个或三个解释变量。与普通最小二乘法相比，使用迭代重加权最小二乘法进行模型校准的敏感性较低，结果更稳健。本文研究了三种校准选项：两种选项考虑了观测的时间顺序，一种选项使用了整个可用数据集的事件随机样本。表现最佳的非线性模型的结果清楚地表明，模型对用于校准的数据集的大小和内容非常敏感。
