## 3 Results
### Experimental Results
#### 3.4.3 Attention Module Analysis of DK-HTAN  
In the structure of the DK-HTAN model, the types of node attention modules and relational attention modules play an important role in the performance of the model. This part of the experiment discusses the performance of node attention types and relationship attention types on DK-HTAN model data in the Qijiang River Basin, in which node attention module types and types of relational attention modules include mean and weighted attention.  
Figures 9 and 10 show the performance impact of different attention-type combinations on the DK-HTAN model. On the Qijiang Basin dataset, the overall performance of different models is similar. However, the type of attention model has a certain impact. The performance of the weighted model is in the middle, although intuitively, the model using DK-HTAN with weighted attention should perform better than the model using only average attention modules. However, in this experiment, the experimental results are different from the expected assumptions: the mean-type relational attention module can enhance the performance of the model more, and the weighted-type node attention module can enhance the outcomes of the model more. In general, weighted attention has a positive effect on model performance, while average attention has a stabilizing effect on model performance. However, due to the error characteristics of neural network model training, the training quality has a certain impact on the performance of the weighted attention module. If the training quality of the weighted focus module is not enough, the performance of the model may not be improved. At the same time, considering that the most important prior relationship has been put into the model when modeling, its mean relational attention module can better reflect the performance of the model. Although in this part of the experiment, the weighted focus method did not always maintain its advantages, it still showed good performance. At the same time, the overall performance of the DK-HTAN model surpassed other baseline models and other defined models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Region** & **Model** & **5\%** & **10\%** & **15\%** & **20\%** \\ \hline \multirow{4}{*}{Qijiang} & SVR & 0.601 (0.361) & 0.589 (0.233) & 0.530 (0.119) & 0.511 (0.012) \\  & RNN & 0.719 (0.412) & 0.646 (0.246) & 0.623 (0.145) & 0.607 (0.023) \\ \cline{1-1}  & LSTM & 0.732 (0.698) & 0.674 (0.259) & 0.660 (0.189) & 0.644 (0.012) \\ \cline{1-1}  & STALSTM & 0.789 (0.634) & 0.721 (0.312) & 0.704 (0.259) & 0.698 (0.031) \\ \cline{1-1}  & AGCLSTM & 0.816 (0.287) & 0.842 (0.189) & 0.760 (0.012) & 0.733 (0.022) \\ \cline{1-1}  & DK-HTAN & 0.833 (0.264) & 0.815 (0.174) & 0.791 (0.014) & 0.763 (0.027) \\ \hline \hline \end{tabular}
\end{table}
Table 4: PICP performance of each model under different proportions of outlier data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Node Attention** & **Relational Attention** \\ \hline DK-HTAN & weighted & weighted \\ DK-HTAN-a & weighted & mean \\ DK-HTAN-b & mean & weighted \\ DK-HTAN-c & mean & mean \\ \hline \hline \end{tabular}
\end{table}
Table 5: Corresponding DK-HTAN model type table.