## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Categories**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Pollutant**} & \multicolumn{5}{c}{**Model Performance Indicators**} \\ \cline{3-8}  & & & **RMSE** & **MAE** & **PCC** & **KTC** \\ \hline \multirow{3}{*}{Deep Learning Model} & \multirow{3}{*}{LSTM} & **PM\({}_{\mathbf{10}}\)** & 16.69 & 14.20 & **0.88** & 0.70 \\  & & PM\({}_{2.5}\) & 9.58 & 8.16 & 0.86 & 0.69 \\  & & NO\({}_{2}\) & 11.64 & 9.32 & 0.86 & 0.71 \\  & & CO & 0.29 & 0.24 & 0.69 & 0.49 \\ \hline \multirow{3}{*}{Machine Learning Model} & \multirow{3}{*}{RF} & \(\mathbf{\mathbf{\Pi}}_{\mathbf{10}}\) & 6.81 & 4.86 & 0.96 & 0.84 \\  & & **PM\({}_{\mathbf{2.5}}\)** & 2.04 & 1.20 & **0.99** & **0.92** \\  & & NO\({}_{2}\) & 6.93 & 4.84 & 0.94 & 0.81 \\  & & CO & 0.12 & 0.09 & 0.82 & 0.58 \\ \hline \multirow{3}{*}{Deep Learning Model} & \multirow{3}{*}{RNN} & **PM\({}_{\mathbf{10}}\)** & 21.16 & 18.06 & **0.78** & **0.58** \\  & & PM\({}_{2.5}\) & 12.76 & 10.35 & 0.67 & 0.48 \\ \cline{1-1}  & & NO\({}_{2}\) & 19.08 & 15.86 & 0.71 & 0.52 \\ \cline{1-1}  & & CO & 0.39 & 0.31 & 0.44 & 0.29 \\ \hline \multirow{3}{*}{Machine Learning Model} & \multirow{3}{*}{SVR} & PM\({}_{\mathbf{10}}\) & 7.45 & 5.63 & 0.95 & 0.82 \\  & & **PM\({}_{\mathbf{2.5}}\)** & 3.52 & 2.46 & **0.96** & **0.84** \\ \cline{1-1}  & & NO\({}_{2}\) & 12.19 & 10.12 & 0.92 & 0.77 \\ \cline{1-1}  & & CO & 0.28 & 0.24 & 0.72 & 0.51 \\ \hline \hline \end{tabular} The best performance in each category is shown in bold.  
\end{table} 
Table 3: _Cont._In addition, the RNN model showed the best performance in predicting PM\({}_{10}\) with the highest PCC and KTC. Also, the LSTM model showed the best performance in predicting PM\({}_{10}\) with the highest PCC. Figure 8 shows the measured and predicted PM\({}_{10}\) concentrations using RNN in 2020 and 2021, which offers a high PCC of 0.78 and a KTC of 0.58. Figure 9 shows the measured and predicted PM\({}_{10}\) concentrations using LSTM in 2020 and 2021, which provides a high PCC of 0.88 and a KTC of 0.70. Figure 5: SHAP values for the RF model of PM\({}_{2.5}\) prediction. Figure 6: Measured and predicted CO concentrations using SVR for 2020 and 2021. Figure 7: Measured and predicted NO\({}_{2}\) concentrations using GRU for 2020 and 2021. Overall, the RF model provided the best prediction for all pollutants, specifically for PM\({}_{10}\) (PCC = 0.96 and KTC = 0.84), PM\({}_{2.5}\) (PCC = 0.99 and KTC = 0.92), NO\({}_{2}\) (PCC = 0.94 and KTC = 0.81), and CO (PCC = 0.82 and KTC = 0.58), respectively, while the SVR model performed the second best in terms of prediction for all pollutants, specifically for PM\({}_{10}\) (PCC = 0.95 and KTC = 0.82), PM\({}_{2.5}\) (PCC = 0.96 and KTC = 0.84), NO\({}_{2}\) (PCC = 0.92 and KTC = 0.77), and CO (PCC = 0.72 and KTC = 0.51), respectively. Table 4 shows the standard deviation (SD) of the average five model runs for the different models. It is essential to assess the SD in terms of the PCC and KTC of the model, especially after five model runs, to ensure the stability and repeatability of the model. The ANN and GRU models have the lowest SD for PM\({}_{2.5}\); the LSTM model has the lowest SD for PM\({}_{10}\); the RF model has the lowest SD for PM\({}_{10}\), PM\({}_{2.5}\), and NO\({}_{2}\); the RNN model has the lowest SD for CO; and the SVR model has the lowest SD for PM\({}_{10}\), PM\({}_{2.5}\), NO\({}_{2}\), and CO. Overall, the SVR model has a 0.00 value with the lowest SD for RMSE, MAE, PCC, and KTC for all pollutants after five model runs, which shows that the SVR models performed the best in terms of stability and repeatability amongst the prediction models applied in this study. The obtained results of this study show that the RF model has the highest PCC and KTC values for the air pollution prediction for all pollutants, including PM\({}_{10}\), PM\({}_{2.5}\), NO\({}_{2}\), and CO, while the SVR model has the lowest SD for all model evaluation metrics, including RMSE, MAE, PCC, and KTC.