## 4 Results and Discussion
### Comparison with Other Models
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CFS-MLP Model** & \multicolumn{2}{c}{**CFS-SVR Model**} & \multicolumn{2}{c}{**CFS-LR Model**} \\ \hline Correlation coefficient & 0.980 & Correlation coefficient & 0.7578 & Correlation coefficient & 0.82 \\ Mean absolute error (ppm) & 0.896 & Mean absolute error (ppm) & 1.972 & Mean absolute error (ppm) & 1.9713 \\ Root mean squared error (ppm) & 1.286 & Root mean squared error (ppm) & 3.7109 & Root mean squared error (ppm) & 3.1057 \\ Relative absolute error (\(\%\)) & 21.99 & Relative absolute error (\(\%\)) & 64.3605 & Relative absolute error (\(\%\)) & 64.333 \\ Root relative squared error (\(\%\)) & 19.40 & Root relative squared error (\(\%\)) & 67.2464 & Root relative squared error (\(\%\)) & 56.2795 \\ Total number of instances & 247 & Total number of instances & 247 & Total number of instances & 247 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The comparison between CFS-MLP, CFS-SVR and CFS-LR models. Figure 8: Accuracy assessment of the predicted maps (morning, afternoon, evening and night). Figure 9: Comparison between the proposed and other models. The air quality data were obtained from fixed air quality stations, the traffic data were collected from vehicle trajectory, and the meteorology data were collected from monitoring stations. Other data used were land use, road geometry and social information. Their results presented the prediction maps with low resolution, cell size (\(1\times 1\)) km\({}^{2}\) in local scale. The limitation of this model can be summarized in some points. The data collection from the standard fixed air quality monitoring stations may not be able to measure the air quality that people are exposed on the ground level due to the limitation of monitoring location and height. On the other hand, the data obtained from the fixed stations are not suitable for high-resolution prediction maps such as microscale maps. Moreover, this study did not contain information about the terrain and buildings. The proposed model in this research is different from the aforementioned study based on some points. We developed a GIS-based NN and data were obtained from a field survey. Moreover, the land use and DSM were extracted from a very high resolution LiDAR data clouds. The proposed methodology is designed to predict the vehicular CO and produce high-resolution maps at a microscale level (\(5\times 5\)) m\({}^{2}\) whereas the aforementioned paper estimated air pollutants based on a low-resolution grid (\(1\times 1\)) km\({}^{2}\). Regarding the meteorology parameters shown in Table 4, it is evident that the correlation between wind speed and vehicular emissions is 0.0016, which is considered weak in the short-term prediction and in a small area compared to other factors. In addition, the temperature has the lowest correlation with vehicular emissions 0.0014; therefore, the variation in the vehicular emissions (i.e., CO) may not be significantly affected by the weather condition in the prediction in small areas. On the other hand, the DSM, which was used to extract the terrain and building's altitude in the study area, has a good correlation with vehicular emissions (more than meteorology parameters). Therefore, the geographic factors are important in prediction studies. This study adopted high-resolution elevation data in order to extract results that are more accurate. In urban areas, building altitude is an important parameter because it can resist vehicular emissions and prevent the distribution of pollutants in urban areas.