# Improving Air Pollution Prediction System through Multimodal Deep Learning Model Optimization
## 6 Experimental Evaluation  
We evaluated the performance of newly proposed models using the dataset described in Section 3. This experiment used Python program language, Tensorflow, Keras, CentOS Linux release 9 July 2009 (Core) for software, and Intel(R) Xeon(R) Silver 4210 CPU @ 2.20 GHz, 125 GB of RAM for hardware. However, we used 90 GB of memory for model learning. LSTM AutoEncoder is denoted as LSTM_AE. In the case of the multimodal deep learning model, the model combining LSTM AutoEncoder and the basic CNN is denoted as Multimodal1, the model combining LSTM AutoEncoder and VGG16 is denoted Multimodal2, and the model combining LSTM AutoEncoder and VGG19 is denoted Multimodal3, respectively. LSTM_AE used only numerical data, and multimodal deep learning models used multimodal data. We used RMSE (Root Mean Square Error) as a performance indicator for the model's performance evaluation. The ratio of training sets/verification sets/test sets is set to 6:2:2. The basic activation function of the LSTM layer was tanh, the cyclic activation function was sigmoid, and the activation function of the dense layer was ReLU, respectively. The optimization function was set to Adam. Table 1 shows the range of hyperparameters considering the accuracy of each model. The contents of Table 1 are applied to katib. For Multimodal2 and Multimodal3, which are LSTM_AE + VGGNet16/19, the maximum batch size is set to 60 because it often diverges infinitely when the batch size exceeds 60, for multimodal deep learning models (Multimodal1, Multimodal2, and Multimodal3), almost all of them diverge infinitely when the epoch exceeds 200, setting the maximum epoch to 200. We conducted 10 experiments on each model, and Table 2 shows the top 5 in order of good performance. For the multimodal deep learning model, numerical data used the same data as LSTM_AE, and all image data used the original image data. This paper compares the performance based on the minimum and average values among the experimental results. Figure 9 displays the minimum Test RMSE (Rank 1) of each model listed in Table 2. The RMSE of LSTM_AE, which processes numerical data, showed a value of 15.96, and the RMSE of Multimodal1, which is LSTM_AE + CNN, was 14.57, decreased by 8.71% compared with LSTM_AE. The RMSE of Multimodal2, LSTM_AE + VGG16, was 14.18, which decreased by 2.68% compared with Multimodal1. The RMSE of Multimodal3, LSTM_AE + VGG19, was 13.96, which decreased by 1.55% compared with Multimodal2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Rank** & **LSTM\_AE** & **Multimodal1** & **Multimodal2** & **Multimodal3** \\ \hline Rank 1 & 15.96 & 14.57 & 14.18 & 13.96 \\ Rank 2 & 16.15 & 14.66 & 14.65 & 14.03 \\ Rank 3 & 16.4 & 15.06 & 14.83 & 14.62 \\ Rank 4 & 17.44 & 15.52 & 15.1 & 14.78 \\ Rank 5 & 18.58 & 17.39 & 15.78 & 15.57 \\ \hline Avg of Rank & 16.91 & 15.44 & 14.91 & 14.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the performance of proposed models using the original image (Test RMSE). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **LSTM\_AE** & **Multimodal1** (**LSTM\_AE** & **Multimodal2** & **Multimodal3** \\  & **+ (Basic) CNN)** & **(LSTM\_AE + VGG16)** & **(LSTM\_AE + VGG19)** \\ \hline Learning rate & 0.00001–0.001 & 0.00001–0.001 & 0.00001–0.001 & 0.00001–0.001 \\ Cell size & 100–400 & 100–400 & 100–400 & 100–400 \\ Batch size & 20–80 & 20–80 & 20–60 & 20–60 \\ Image size & 120–140 & 120–140 & 120–140 & 120–140 \\ Epoch & 50–300 & 50–200 & 50–200 & 50–200 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The range of hyperparameter settings for each model. Figure 10 shows the average Test RMSE (Average of Rank) of each model shown in Table 2. The RMSE of LSTM_AE, which processes numerical data, showed a value of 16.91, and the RMSE of Multimodal1, which is LSTM_AE + CNN, was 15.44, decreased by 8.69% compared with LSTM_AE. The RMSE of Multimodal2, LSTM_AE + VGG16, was 14.91, which decreased by 3.43% compared with Multimodal1.