Fuzzy decision support system to the prediction of ozone concentrations

Artificial neural networks are used to model the interactions that occur between ozone concentration and environment data. A number of generic methods for analysis and modeling are investigated. Because the mechanism of the ozone concentration is highly complex, nonlinear, and nonstationary, the modeling methods of an ozone prediction system have many problems and the results of prediction are not of good performance so far, especially in the high-level ozone concentration. This paper introduces a modeling method of the ozone prediction system using neuro-fuzzy approaches and fuzzy clustering method. The dynamic polynomial neural network (DPNN) based upon a typical algorithm of GMDH (group method of data handling) is employed for data analysis, identification of nonlinear complex system, and prediction of the ozone concentration. The proposed prediction system is applied to the 19 areas in Seoul, Korea, and the final results are discussed in the senses of root mean-square error (RMSE) and R-square