用于地面臭氧预测的非线性回归模型与神经网络模型的比较
A comparison of nonlinear regression and neural network models for ground-level ozone forecasting.
作者信息
Cobourn W G, Dolcine L, French M, Hubbard M C
机构信息
Department of Mechanical Engineering, Speed Scientific School, University of Louisville, Kentucky, USA.
出版信息
J Air Waste Manag Assoc. 2000 Nov;50(11):1999-2009. doi: 10.1080/10473289.2000.10464228.
A hybrid nonlinear regression (NLR) model and a neural network (NN) model, each designed to forecast next-day maximum 1-hr average ground-level O3 concentrations in Louisville, KY, were compared for two O3 seasons--1998 and 1999. The model predictions were compared for the forecast mode, using forecasted meteorological data as input, and for the hindcast mode, using observed meteorological data as input. The two models performed nearly the same in the forecast mode. For the two seasons combined, the mean absolute forecast error was 12.5 ppb for the NLR model and 12.3 ppb for the NN model. The detection rate of 120 ppb threshold exceedances was 42% for each model in the forecast mode. In the hindcast mode, the NLR model performed marginally better than the NN model. The mean absolute hindcast error was 11.1 ppb for the NLR model and 12.9 ppb for the NN model. The hindcast detection rate was 92% for the NLR model and 75% for the NN model.
一个混合非线性回归（NLR）模型和一个神经网络（NN）模型，均旨在预测肯塔基州路易斯维尔市次日1小时平均地面臭氧（O3）浓度的最大值，针对1998年和1999年两个臭氧季节进行了比较。针对预测模式（使用预测气象数据作为输入）和后报模式（使用观测气象数据作为输入）对模型预测结果进行了比较。在预测模式下，这两个模型的表现几乎相同。对于这两个季节的综合情况，NLR模型的平均绝对预测误差为12.5 ppb，NN模型为12.3 ppb。在预测模式下，每个模型对于120 ppb阈值超标情况的检测率均为42%。在后报模式下，NLR模型的表现略优于NN模型。NLR模型的平均绝对后报误差为11.1 ppb，NN模型为12.9 ppb。NLR模型的后报检测率为92%，NN模型为75%。
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