## 4 Results and Discussion
### Comparison with Other Models
The correlation coefficient based on our proposed model was 0.980, which is higher than the SVR (0.8668) and LR (0.851). The MAE of the proposed model was 0.896 ppm which is lower than SVR (1.640 ppm) and LR (1.851 ppm). On the other hand, the RMSE results indicated that the proposed model has the lowest RMSE (1.286 ppm) compared to SVR (2.752 ppm) and LR models (2.849 ppm). The RAE ratio for the proposed model was calculated to be 21.99%, which was lower than the RAE ratio of SVR (51.646%) and LR (55.048%). The root relative squared error indicated that the proposed model has the lowest value (19.40%) among the other models (49.784% and 48.292%). Table 8 shows the results of the prediction models when combining the correlation-based feature selection; the correlation coefficients of the CFS-SVR and CFS-LR are 0. 0.7578 and 0.82, respectively. These values indicated that there is decrease in the correlation coefficient. The MAE increased for the CFS-SVR and CFS-LR and reached 1.972 ppm and 1.9713 ppm, respectively. The RMSE also increased to 3.7109 ppm and 3.1057 ppm. Figure 8 illustrates the variations of the traffic CO concentrations in the testing data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CFS-MLP Model** & \multicolumn{3}{c}{**SVR Model**} & \multicolumn{2}{c}{**LR Model**} \\ \hline Correlation coefficient & 0.980 & Correlation coefficient & 0.8668 & Correlation coefficient & 0.851 \\ Mean absolute error (ppm) & 0.896 & Mean absolute error (ppm) & 1.640 & Mean absolute error (ppm) & 1.851 \\ Root mean squared error (ppm) & 1.286 & Root mean squared error (ppm) & 2.752 & Root mean squared error (ppm) & 2.849 \\ Relative absolute error (\%) & 21.99 & Relative absolute error (\%) & 51.646 & Relative absolute error (\%) & 55.048 \\ Root relative squared error (\%) & 19.40 & Root relative squared error (\%) & 49.784 & Root relative squared error (\%) & 48.292 \\ Total number of instances & 247 & Total number of instances & 247 & Total number of instances & 247 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The comparison between CFS-MLP, SVR and LR models. The first model that we compared with one of the baseline models is the CALINE4 model. The ALINE4 model is a dispersion model, which depends on a plume dispersion model used to predict the vehicular CO on roadways [48]. The CALINE4 model simulates the data based on a Gaussian diffusion algorithm and characterizes the pollutants dispersion on roads. We defined the proposed road network, weather data, traffic flow information, and receptor locations, and the prediction of traffic CO emissions was obtained. The MAE and RMSE values were 2.376 ppm and 4.2254 ppm, respectively, whereas and the correlation coefficient value was 0.6504. The prediction results appeared worse than our proposed model. This may due to the fact that the Gaussian diffusion, which was assumed in CL4, is not very realistic. We also compared our work with the Land Use Regression (LUR) Model. This model is generally applied to predict air pollutants depending on the land use, traffic flow information, meteorology data and combined them based on a linear regression algorithm. The LUR model showed the following values: MAE 2.21 ppm, RMSE 4.50 ppm and a correlation coefficient of 0.5989 (Figure 9). The final LUR equation used is given below:  
Predicted CO = 0.0018 \(\times\) Car + 0.0423 \(\times\) HV \(-\) 0.0219 \(\times\) Motorbike + 0.2211 \(\times\) Temp/C + 0.0312 \(\times\) Relative Humidity \(-\) 0.1315 \(\times\) Wind speed + 0.0018 \(\times\) Wind Angle Degree \(-\) 0.0232 \(\ast\) DSM + 0.0006 \(\times\) Builtup area + 0.0064 \(\times\) Highway \(-\) 8.6627. There are many models developed based on GIS and machine learning, for example [26] designed a model based on the integration of the ANN algorithm and the linear-chain conditional random field algorithm to produce real-time and fine-grained air pollution prediction maps.