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http://dx.doi.org/10.5322/JES.2009.18.2.129

Prediction of Daily Maximum SO2 Concentrations Using Artificial Neural Networks in the Urban-industrial Area of Ulsan  

Lee, So-Young (Forecast Research Laboratory, National Institute of Meteorological Research, KMA)
Kim, Yoo-Keun (Division of Earth Environmental System, Pusan National University)
Oh, In-Bo (Division of Earth Environmental System, Pusan National University)
Kim, Jung-Kyu (Tae-hwa River Managing Agency, Ulsan metrocity)
Publication Information
Journal of Environmental Science International / v.18, no.2, 2009 , pp. 129-139 More about this Journal
Abstract
Development of an artificial neural network model was presented to predict the daily maximum $SO_2$ concentration in the urban-industrial area of Ulsan. The network model was trained during April through September for 2000-2005 using $SO_2$ potential parameters estimated from meteorological and air quality data which are closely related to daily maximum $SO_2$ concentrations. Meteorological data were obtained from regional modeling results, upper air soundings and surface field measurements and were then used to create the $SO_2$ potential parameters such as synoptic conditions, mixing heights, atmospheric stabilities, and surface conditions. In particular, two-stage clustering techniques were used to identify potential index representing major synoptic conditions associated with high $SO_2$ concentration. Two neural network models were developed and tested in different conditions for prediction: the first model was set up to predict daily maximum $SO_2$ at 5 PM on the previous day, and the second was 10 AM for a given forecast day using an additional potential factors related with urban emissions in the early morning. The results showed that the developed models can predict the daily maximum $SO_2$ concentrations with good simulation accuracy of 87% and 96% for the first and second model. respectively, but the limitation of predictive capability was found at a higher or lower concentrations. The increased accuracy for the second model demonstrates that improvements can be made by utilizing more recent air quality data for initialization of the model.
Keywords
$SO_2$; Urban-industrial area; Potential parameters; Artificial neural network; Cluster analysis;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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