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KMID : 1024520090180020129
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Journal of the Environmental Sciences
2009 Volume.18 No. 2 p.129 ~ p.139
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Prediction of Daily Maximum Concentrations Using Artificial Neural Networks in the Urban-industrial Area of Ulsan
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Lee So-Young
Kim Yoo-Keun
Oh In-Bo
Kim Jung-Kyu
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Abstract
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Development of an artificial neural network model was presented to predict the daily maximum concentration in the urban-industrial area of Ulsan. The network model was trained during April through September for 2000-2005 using potential parameters estimated from meteorological and air quality data which are closely related to daily maximum concentrations. Meteorological data were obtained from regional modeling results, upper air soundings and surface field measurements and were then used to create the 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 concentration. Two neural network models were developed and tested in different conditions for prediction: the first model was set up to predict daily maximum 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 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.
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KEYWORD
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Urban-industrial area, Potential parameters, Artificial neural network, Cluster analysis
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