# Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas
## 3 Results
### Model Validation  
The best-performing model, RFR, is validated by real-time monitoring of the MA-AQI system. The validation process spans from 1 March 2024 to 1 July 2024. The input feature values were acquired in real-time and fed into the MA-AQI model for \(AQI\) prediction during this period. The model's output values were then compared with the official \(AQI\) values reported by authorities. As shown in Figure 12, comparing the predicted \(AQI\) values generated by the RFR model, and the official \(AQI\) measurements reveals a significant  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Algorithm** & \(R^{2}\) & \(MAE\) [\%] & \(MAP\) [\%] & \(MedAE\) [\%] & \(OP\) \\ \hline \multicolumn{6}{c}{Implemented and customized ML algorithms} \\ \hline RFR & 0.9944 & 0.2237 & 0.0068 & 0.0399 & 0.95618 \\ DTR & 0.9791 & 1.3196 & 0.0475 & 1.0153 & 0.627155 \\ LR & 0.9186 & 3.1369 & 0.1070 & 2.6027 & 0.0744 \\ KNR & \(-\)0.175 & 12.155 & 0.4481 & 11.0000 & \(-\)3.07768 \\ SVR & 0.9902 & 1.1273 & 0.0391 & 1.2282 & 0.631025 \\ GBR & 0.9940 & 0.4259 & 0.0181 & 0.1696 & 0.902845 \\ \hline \multicolumn{6}{c}{ML-NET integrated algorithms} \\ \hline FastForest & 0.9482 & 2.7009 & 3.3538 & 10.1051 & \(-\)1.94776 \\ FastTree & 0.9634 & 0.9753 & 2.8193 & 2.6264 & \(-\)0.400685 \\ FastTreeTweedie & 0.9744 & 0.7510 & 2.3555 & 2.0555 & \(-\)0.137865 \\ Gam & 0.9079 & 2.8480 & 4.4729 & 10.2693 & \(-\)2.346305 \\ LbfgsPoissonRegression & \(-\)1.5016 & 19.6176 & 23.3069 & 68.3076 & \(-\)20.18149 \\ OnlineGradientDescent & \(-\)4.0409 & 29.6999 & 33.0849 & 78.9704 & \(-\)27.242375 \\ \hline \multicolumn{6}{c}{Implemented and customized DL algorithms} \\ \hline FNNs & \(-\)0.0026 & 11.8325 & 0.4199 & 11.3324 & \(-\)3.001745 \\ TCNs & 0.9934 & 0.5235 & 0.0172 & 0.3395 & 0.86275 \\ RNNs & 0.9957 & 0.7009 & 0.0235 & 0.6142 & 0.793965 \\ LSTMs & 0.9788 & 1.5709 & 0.0661 & 1.4351 & 0.52079 \\ GRNNs & 0.9939 & 0.8900 & 0.0317 & 0.7995 & 0.734625 \\ TDNNs & \(-\)0.1785 & 12.483 & 0.4254 & 10.417 & \(-\)3.03402 \\ DBNs & 0.9973 & 0.5520 & 0.0182 & 0.4556 & 0.84232 \\ \hline \end{tabular}
\end{table}
Table 3: Performance results of \(AQI\) prediction algorithms on training and evaluation sets.  
correlation. The average deviation between the expected and actual reported values was 0.58%, indicating that the model's predictions were highly accurate and closely aligned with real-world data. This strong performance further supports the MA-AQI model in predicting AQ based on real-time data inputs.  
The chart presented in Figure 12 compares predicted \(AQI\) values with actual \(AQI\) measurements. The bar chart shows that the predicted values reproduce the real data with fidelity. The accuracy of the MA-AQI model demonstrated its applicability in production or commercial scenarios. The slight variations between the two sets of values are minimal, indicating that the predictions generated by the model are reliable for real-world applications. Variations of 1 or 2 units in \(AQI\) values are not a significant concern, as such minor differences fall within the acceptable margin of error for AQ measurements and do not substantially impact the interpretation of \(AQI\).