## 4 Results and Discussions
### Performance of Models after Adding CMAQ Data at All Locations  
Figures 5-10 show the actual and forecast results for each model. Figure 11 shows the results of all models and the numerical analysis are subsequently provided in Tables 5-8. In Figure 5, it can be noticed that, at Stations 2 and 4, XGBoost is not predicting the peak values at some points. In Figure 6, LightGBM has difficulty in predicting the actual values, especially at Stations 1, 2, 4 and 7 where it is showing a wide difference between actual and predicted values. Results of GRU are shown in Figure 7, which shows it is unable to predict the real values at Stations 1, 2, and 4. CNNLSTM in Figure 8 is providing good predictions only at Stations 3 and 8, while, at Station 4, its predictions results are deviating from original values. BiULSTM prediction and actual values are drawn in Figure 9. On average, it is showing better results than any other model; however, at Station 4, it is unable to detect the peak values. LSTM prediction results are shown in Figure 10; it gives better results than all models except at Stations 2 and 7, where the BiULSTM model error values are lower. Overall, LSTM is performing well by giving fewer error values and a higher \(IA\). The \(MAE\), \(RMSE\), \(R\), and \(IA\) values for all models after adding CMAQ data are given in Tables 5-8. Table 5 provides the \(MAE\) values for each model. From the experiments, the BiULSTM model for Stations 1, 2, and 7 gives the lowest \(MAE\) values; for all other stations, LSTM evaluation results are the best. The average \(MAE\) values for all station in the case of LSTM are also the lowest, i.e., 3.5847 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\), followed by BiULSTM (3.6246 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)), GRU (3.9533 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)), CNNLSTM (3.9857 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)), XGBoost (4.3386 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)), and LightGBM (4.7792 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)), in decreasing order. In terms of \(RMSE\) provided in Table 6, LSTM gives the lowest scores at every station except for Station 2, where BiULSTM model gives the lowest error value. The average \(RMSE\) ranking for models from lowest to highest is: LSTM (4.8292 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)), BiULSTM (4.9168 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)), GRU (5.3546 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)), CNNLSTM (5.3643 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)), XGBoost (5.9037 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)), and LightGBM (6.5343 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)). Values of \(R\) are given in Table 7. BiULSTM network shows the highest score at Station 6; however, \(R\) values for LSTM for all other stations are highest as compared to other models. The average \(R\) scores from highest to lowest are: LSTM (0.8989) > BiULSTM (0.8927) > CNNLSTM (0.8668) > GRU (0.8640) > XGBoost (0.8350) > LightGBM (0.8304). In terms of \(IA\) listed in Table 8, BiULSTM network gives the highest value only at Station 2 (0.9065). \(IA\) values for LSTM network are the highest at all other stations. The average \(IA\) score from highest to lowest are: LSTM (0.9368) > BiULSTM (0.9334) > CNNLSTM (0.9084) > XGBoost (0.8992) > GRU (0.8905). From the results of our experiments, the \(MAE\) and \(RMSE\) values of LSTM network are the lowest while correlation coefficient \(R\) and \(IA\) are the highest, which shows that this model performs well on this dataset. BiULSTM network is the next best after LSTM, considering all metrics of evaluation. There are still the following limitations:  
1. The observation period for this study is only one year. If more data were provided, the network would have better capability to understand the spatial and temporal dependencies.