Prediction of daily maximum ozone concentrations from meteorological conditions using a two-stage neural network

Meteorological conditions exert large impacts on ozone concentrations. Predicting ozone concentrations from meteorological conditions is a very important issue in air pollution. A self-organizing map (SOM) neural network is suitable for clustering data because of its visualization property. A multilayer perceptron (MLP) neural network was widely used recently in predicting air pollutant concentrations since MLP can capture the complex nonlinear concentration-meteorology relationship. In this work, a two-stage neural network (model I) was developed and used to predict ozone concentrations from meteorological conditions. The two-stage neural network first utilized an unsupervised neural network (two-level clustering approach: SOM followed by K-means clustering) to cluster meteorological conditions into different meteorological regimes. It was found that ozone concentrations within most meteorological regimes exhibited significantly different concentration characteristics. Then a supervised MLP neural network was used to simulate the nonlinear ozone-meteorology relationship within each meteorological regime. The results showed that meteorological conditions can explain at least 60% variance of ozone concentrations by the two-stage neural network. In addition, three other models (model II: multiple linear regressions (MLR), model III: two-level clustering approach followed by MLR and model IV: MLP) were also utilized to predict ozone concentrations, and were compared with model I. The sequence of predicted accuracy was model I > model IV > model III > model II, suggesting that the two-stage neural network had the best prediction performance among the four models and can elucidate better the dependence of ozone on meteorology than other models. (c) 2005 Elsevier B.V. All rights reserved.