|
Abstract:The assessment of artificial neural network models-sigmoid (ANN-sigmoid) and
hyperbolic tangent (ANN-tanh) for real-time air pollution forecasting in a Korean coastal
city was performed using 15 input independent variables (3 hours' earlier PM, gas and
meteorological data influenced by 48 hours’ earlier PM and gas data of a Chinese city). A
feed-forward ANN technique of multilayer perception (MLP) with back-propagation training
algorithm for error calculation was adopted with 15 hidden nodes and each prediction
formula on four output variables was suggested. Root mean square error (RMSE) and the
coefficient of determination (R2
) assess each model’s prediction ability between the
predicted and measured values, before, during, and after the Yellow Dust period (March
18~27). Pearson R coefficients from the ANN-sigmoid (or ANN-tanh) model on PM10, PM2.5,
NO2, and O3 were 0.907 (0.935), 0.860 (0.942), 0.860 (0.925), and 0.957 (0.946) before the
dust period, 0.917 (0.943), 0.959 (0.969), 0.855 (0.853), and 0.954 (0.949) during its period,
and 0.920 (0.947), 0.928 (0.938), 0.917 (0.896), and 0.923 (0.952) after its period, showing
very high prediction accuracy overall. Scatter plots with empirical formulae and temporal
distributions between the predicted and measured values showed excellent prediction
performance by two models, and the ANN-tanh model produced more accurate results.
|