Artificial Neural Network Models for Daily PM10 Air Pollution Index Prediction in the Urban Area of Wuhan, China

Dust storm is a critical remote source that causes low air quality in many cities in China. The prediction accuracy of high particulate matter with a diameter <10 mu m(PM10) air pollution index (API) event caused by dust storm is low in China. To solve this problem, dust storm data from northern China was first used to tune the Elman-based forecast model to predict the daily PM10 API with a lead time of 1 day. Effectiveness of this forecaster was tested using a time series recorded from September 1, 2001, to December 31, 2007, at six monitoring stations located within the urban area of Wuhan, China. Experimental trials show that the improved Elman model provides low root mean square error values and mean absolute error values in comparison to the standard Elman model. In addition, higher coefficient of determination (r(2) = 0.62) and accuracy rate (83.33%) values were realized for the improved Elman model in comparison to the standard Elman model (r(2) = 0.22, accuracy rate = 64.81%) when predicting high PM10 API events caused by dust storms.