Forecasts Usinq Neural Network versus Box-Jenkins Methodoloqy for Ambient Air Quality Monitoring Data

摘要

This study explores ambient air quality forecasts using the conventional time-series approach and a neural net- work. Sulfur dioxide and ozone monitoring data collected from two background stations and an industrial station are used. Various learning methods and varied numbers of hidden layer processing units of the neural network model are tested. Results obtained from the time-series and neural network models are discussed and compared on the basis of their performance for 1-step-ahead and 24-step-ahead forecasts. Although both models perform well for 1-step-ahead prediction, some neural network results reveal a slightly better forecast without manually adjusting model parameters, according to the results. For a 24-step-ahead forecast, most neural network results are as good as or superior to those of the time-series model. With the advantages of selflearning, selfadaptation, and parallel processing, the neural network approach is a prom- ising technique for developing an automated short-term ambient air quality forecast system.