# Prediction of PM2.5 Concentration on the Basis of Multitemporal Spatial Scale Fusion
## 3 Results
### Comparison with Other Methods  
SVR, LSTM, LSTME, T-LSTME, and ConvLSTM are some popular models. This article selects these models to compare with the proposed models to evaluate their performance. LSTME and TS-LSTME models consider past hourly PM2.5 levels, daily meteorological \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{2}{c}{**Prediction Day = 1**} & \multicolumn{2}{c}{**Prediction Day = 3**} & \multicolumn{2}{c}{**Prediction Day = 7**} \\ \cline{2-7}  & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** \\ \hline
1 & 12.62 & 11.34 & 13.92 & 12.91 & 15.87 & 13.68 \\
2 & 11.21 & 11.27 & 13.65 & 12.62 & 14.93 & 13.07 \\
3 & 10.79 & 10.23 & 12.47 & 10.86 & 13.86 & 12.81 \\
4 & 11.06 & 11.09 & 14.39 & 11.55 & 14.33 & 12.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Prediction results with different structures. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Input**} & **Prediction** & **Input** & **Prediction** & **Input** & **Prediction** \\
**Lengths** & **Day = 1** & **Lengths** & **Day = 3** & **Lengths** & **Day = 7** \\ \hline
2, 4, 6 & 13.25 & 6, 9, 12 & 14.46 & 14, 21, 28 & 15.68 \\
3, 6, 9 & 12.68 & 9, 12, 15 & 14.23 & 21, 28, 35 & 15.36 \\
4, 8, 12 & 10.56 & 12, 15, 18 & 13.25 & 28, 35, 42 & 14.23 \\
5, 10, 15 &information, geographic factors, and human behavior, but do not factor in air quality monitoring. MLR, SVR, and LSTM models consider only past hourly PM2.5 levels. ConvLSTM has some capacity to extract the spatial-temporal connection from the spatial correlation between stations. The performance of each approach is evaluated by comparing the RMSE and MAE of the predicted results. Table 4 demonstrates that ConvLSTM and the novel approach presented in this study outperform SVR, LSTM, and LSTME in forecasting PM2.5 levels. The significance of spatial correlation in PM2.5 prediction is underscored by this finding. Additional analysis of RMSE and MAE reveals that the proposed model has a lower prediction error compared to ConvLSTM, primarily because ConvLSTM lacks adequate modeling capabilities and struggles to capture long-range spatial correlations with fixed convolution kernels. In Figure 5, We display the daily performance (MAE) of several models for 30 air quality monitoring sites in the province of Jilin. The outcomes demonstrate that the suggested model outperforms the SVR, LSTM, LSTME, and ConvLSTM models. The proposed model performs better than the ConvLSTM model in terms of accuracy at the majority of air quality monitoring locations. We choose the predicted data from the 1001A station for presentation from 26 February 2022 to 18 March 2022 to present the findings in an understandable manner. Gray represents the actual data value, while brown, blue, green, purple, and red represent the projected or predicted values from the different models used in this study. The data visualizations clearly show that while the prediction accuracy of the other two models aligns well with the actual data when the prediction interval is small, the model introduced in this study outperforms them in terms of accuracy. Simultaneously, the model presented in this study also surpasses the other two models in peak forecasting performance. Figures 6-8 show that the longer the forecast period, the lower the accuracy of predicting peak values and future trends for each model. At a 7-day prediction time step, the forecast patterns of LSTM and