Forecasts using neural network versus box-jenkins methodology for ambient air quality monitoring data

Jehng-Jung Kao, Shang Shuang Huang

研究成果: Article同行評審

33 引文 斯高帕斯(Scopus)

摘要

This study explores ambient air quality forecasts using the conventional time-series approach and a neural network. 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 self-learning, self-adaptation, and parallel processing, the neural network approach is a promising technique for developing an automated short-term ambient air quality forecast system.

原文English
頁(從 - 到)219-226
頁數8
期刊Journal of the Air and Waste Management Association
50
發行號2
DOIs
出版狀態Published - 1 一月 2000

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