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Title: Short-term prediction of SO2 concentration in Macau with artificial neural networks
Authors: Mok, Kai Meng (莫啟明)
Tam, Sik Chung (譚錫忠)
Issue Date: Nov-1998
Publisher: Elsevier
Citation: Energy and buildings, Nov. 1998, v. 28, no. 3, p. 279-286
Abstract: The air quality of Macau is deteriorating in the recent years due to the rapid economic and population growth of itself and its surrounding areas. One of the main air pollutant which is of great concern to Macau, as well as to many urban cities in the world, is sulfur dioxide (SO2). In view of the yearly averaged SO2 concentration, it is found that Macau may be classified as an uncontaminated area. Nevertheless, the daily averaged SO2 concentrations of the year 1995 show that 39% of the last 3-month values recorded at A. Preta exceeded the Chinese primary standard, and it is selected as the representing period for the investigation of SO2 pollution in Macau. Using just the records of the past month, a three-layered feed-forward artificial neural networks is developed to predict the daily SO2 concentration 5 days in advance. The results show that the accuracy of the ANN model is within 14.45% and 13.71% for two testing periods, respectively. The promising results indicate that the ANN could be used to develop efficient air-quality analysis and prediction models in the future.
ISSN: 0378-7788
Keywords: Macau
Environmental issue
Air pollution
Sulfur dioxide
Artificial neural networks
Time-series prediction
Access: View full-text via DOI

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