# A Novel Interpretable Deep Learning Model for Ozone Prediction
## 3 Results  
We executed the six models mentioned above in each monitoring station in each city to predict the ozone levels in the subsequent 24 h. In each station, all models were trained and tested on the same training and testing datasets, respectively. The RMSE, R2 and SMAPE of each model from the nine stations are shown in Figures 3 and 4. Figure 3 shows the performance of each model in the Nanjing and Beijing cities, while Figure 4 shows the performance of each model in Guangzhou, Wuhan and Chengdu. The comparisons between the different types of models and between LSTM and GRU within each model were then analyzed. As shown in the two figures, the performance of all these models declines in the early stages and then gradually becomes stable after 12 h. As shown in the figures, the Seq2 Seq-based model performed well in the early prediction stage, then it rapidly declined. Comparing LSTM and GRU, GRU generally performed better than LSTM. As for the Seq2 Seq+Attention-based model, the performance was more \begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment Category** & **Experiment Name** & **Description** \\ \hline Seq2 Seq & Seq2 Seq\_LSTM & Encoder–Decoder framework with recurrent component LSTM/GRU. \\  & Seq2 Seq\_GRU & \\ \hline Seq2 Seq+Attention & Seq2 Seq\_LSTM+Attention & Seq2 Seq\_LSTM/GRU with single attention mechanism. \\  & Seq2 Seq\_GRU+Attention & \\ \hline Spatiotemporal attentive & STALSTM & Spatiotemporal attentive model with recurrent component LSTM/GRU \\ \hline \hline \end{tabular} Note that Seq2 Seq-based models are the combination of an Encoder–Decoder framework and LSTM or GRU and Seq2 Seq+Attention applies a single attention mechanism based on Seq2 Seq-based models. Spatiotemporal attentive-based methods include STAGRU and STALSTM. The settings of all models were consistent.  
\end{table} 
Table 1: Experimental design. Figure 3: The performance of each model from the nine monitoring stations of the Nanjing and Beijing cities. The horizontal axis represents the prediction step and the vertical axis represents a specific metric. The solid line is the mean performance for each model from the nine stations. The shaded area represents the variation range in performance, where the upper bound is the maximum and the lower bound is the minimum. stable and errors accumulated more slowly than the Seq2 Seq-based models. Considering our spatiotemporal-based model, the STAGRU performed best in the Nanjing and Beijing cities. The two spatiotemporal models performed similarly and made better predictions than the other models in the Chengdu, Guangzhou and Wuhan cities. Generally, the error of the RNN-based models, especially under the Encoder-Decoder framework, accumulates as the forecast time increases; this is because the relevance between the past observation values and prediction values becomes weaker and thus the recursivity of the Encoder-Decoder framework makes further errors. The predictions of the Seq2 Seq-based methods are generally adequate during the early forecasting hour; however, they begin to deteriorate at a much faster rate than the other methods as forecast time increases. The reason for this is that the only connection between the prediction and the past sequence is the last decoding hidden state, which cannot afford enough information to support forecasting due to the fixed length of the hidden state, thus making errors accumulate rapidly. Therefore, the fitting ability of the Seq2 Seq-based model is unsatisfactory. The Seq2 Seq+Attention-based methods introduce the attention mechanism to link each prediction with all past moments, which complements the shortage of Seq2 Seq-based methods and generates a better model with improved performance. With the support of temporal information, Seq2 Seq+Attention-based methods can significantly improve forecasting skills. However, Seq2 Seq+Attention-based methods fluctuate as forecasting goes on.