Air Quality - Models and Applications by D. Popovic

By D. Popovic

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46 Air Quality - Models and Applications Fig. 6. As in Figure 5, but for the model with 15 neurons in the hidden layer. The performance of the two networks is very similar. However, it is clear that it is difficult for both models to detect ozone concentrations exceeding the standard, which is important for this study. In order to remedy the estimation problem in the high concentration range, it was decided to scale the value estimated by the network. Thus, the final estimated value of ozone (O3) is calculated as Oˆ 3   Oˆ 3 (9) where α is the scaling factor and O3 is the ozone concentration (ppm) estimated by the neural network model.

In training, the three performance criteria mentioned above were used; mean square error (MSE), mean squared error with regularization (MSEREG) and error sum of squares (SSE). m. , and daily average values of Wind Speed (km/h), Humidity (%) Pressure (mbar) and Precipitation (mm) for the period 1999–2004. The database contained 2065 validated data for each variable. 1 Training of ANN models The models were trained on 1990–2004 data. Real and predicted O3 values were classified into three concentration ranges according to the NOM-020-SSA1-1993 standard, quantifying the number of data falling within each range (percentage of correct answers) in order to estimate the performance of each model.

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