## 5 Result and Discussion  
The proposed Multi-Dense Layer BiLSTM model was used to predict PM2.5 concentrations one hour, two hours, and three hours ahead of the three-day observation, totaling 259,200 s. To test the validity of predictions and support preventive maintenance over a longer timeframe, we set the time forecast into hourly, 2 h, and 3 h.  
The proposed Multi-Dense Layer BiLSTM retains low MSE, MAE, and MAPE levels at varied sampling rates, meaning that forecasting accuracy may be assured. Table 3 shows that when the number of epochs in the RNN, LSTM, CNN-LSTM, and Single-Dense Layer BiLSTM algorithms increased, so did the MSE, MAE, and MAPE values, indicating that the three approaches were overfitting. The MSE, MAE, and MAPE values of our approach model, on the other hand, dropped as the prediction time length was increased. These \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hyperparameter** & **RNN** & **LSTM** & **CNN-LSTM** & \begin{tabular}{c} **Single-Dense** \\ **Layer BiLSTM** \\ \end{tabular} & \begin{tabular}{c} **Multi-Dense** \\ **Layer BiLSTM** \\ \end{tabular} \\ \hline Model nodes & 2 RNN nodes & 64 LSTM nodes & 128 LSTM nodes & \begin{tabular}{c} 64 BiLSTM \\ nodes \\ \end{tabular} & \begin{tabular}{c} 64 BiLSTM \\ nodes \\ \end{tabular} \\ \hline Epoch & 20/35/50 & 20/35/50 & 20/35/50 & 20/35/50 & 20/35/50 \\ \hline Batch size & 16 & 64 & 16 & 16 & 16 \\ \hline Interpolate method & linear & linear & N/A & linear & linear \\ \hline \begin{tabular}{c} Train data \\ (\% dataset) \\ \end{tabular} & 64 & 64\% & 64\% & 80\% & 80\% \\ \hline \
\begin{tabular}{c} Validation data \\ (\% dataset) \\ \end{tabular} & 16\% & 16\% & 16\% & N/A & N/A \\ \hline
\begin{tabular}{c} Test data \\ (\% dataset) \\ \end{tabular} & 20\% & 20\% & 20\% & 20\% & 20\% \\ \hline Optimizer & ADAM & SGD & ADAM & ADAM & ADAM \\ \hline Activation & Linear & Linear & ReLU & Linear & Sigmoid \\ \hline Learning rate & 0.01 & 0.01 & 0.001 & 0.001 & 0.001 \\ \hline Dense layer & N/A & N/A & 3 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The list of hyperparameters values for the Multi-Dense Layer BiLSTM Method. findings acknowledge that, compared to other models, our proposed model predicts PM2.5 concentrations 1 h, 2 h, and 3 h ahead of time and has reached the most preciseness. Figure 6a-f display the results of predicting PM2.5 concentration one hour ahead, using the Multi-Dense Layer BiLSTM algorithm. From three kinds of epochs experiments, the optimum result between train and validation loss is revealed before the 10 th epoch. The blue line in Figure 6a,c,f with the log scale indicates train loss. In contrast, the orange line reflecting validation loss is shown to have the same trend, resulting in the proposed model having the lowest MSE, MAE, and MAPE in 1 h ahead prediction, as mentioned in Table 3. From Figure 6b,d,f, the test data, and prediction data are united very closely. These outputs demonstrate that the proposed model has the best fit to predict PM2.5 concentrations. Compared to the other four models, the results of prediction by the proposed model were the closest to the actual value. The loss function patterns of the proposed model to estimate PM2.5 concentration for the following two hours are shown in Appendix A, Figure A1a-f. Especially in Figure A1e, the blue and orange lines were closer in epoch 7 th, then separated wider until epoch 50 th, with relatively having the same trend between these two lines. The test data and prediction data have practically merged in Figure A1b,d,f. These findings suggest that the proposed model is the most accurate in predicting PM2.5 concentrations for 2 h ahead, as mentioned in Table 3.  
Similar to the previous experiments one h ahead and two hours ahead, Figure A2a-f depicts the fitting patterns of three hours ahead prediction by the proposed model has attained the best fit.