## 3 Results
### Experimental Results  
#### 3.4.1 Comparison  
To further corroborate the advantages of the model proposed in this research, the graphical representation in Figure 8 is used to show the PI quality of the six models contrasted. The contrast of PICP, PINRW and TIME of all models is as follows. The SVR model is the basic machine learning prediction model. By confronting the proposed model with baseline models, we discovered that both the PICP and PINRW indices are exceededby the suggested model. Since PINRW and PICP are two opposing objects, the optimal solution chosen must have a PICP that is hardly more than 90% in order to ensure that its PINRW index is low. Due to the seasonality of flood data, we divided the data into flood season and non-flood season for experiments.  
ANN models of fusion tensor decomposition are instrumented to further verify the high PI quality of the proposed model. Table 1 shows that RNN, LSTM, STALSTM, AG-CLSTM and the proposed model are at the same extent in terms of PICP, while the proposed model has a scarcely higher PICP index. Compared to the STALSTM and AGCLSTM models with spatiotemporal characteristics, it is improved 7.49% and 11.33% on the PICP index, respectively. The tensor decomposition method is used for the interpolation of the model preprocessing and the error factor adjustment part of the final output result to solve the nonlinear and nonstationary characteristics of the data, and the PI of DK-HTAN model is significantly smaller than RNN, LSTM, STALSTM, AGCLSTM. Therefore, it can be concluded that DK-HTAN is more suitable for solving the interval stacking problem caused by tensor decomposition. In the index of PINRW, DK-HTAN is improved by 4.08% and 11.26% over the AGCLSTM and STALSTM models.  
Compared to the STALSTM and AGCLSTM models with spatiotemporal characteristics, DK-HTAN is improved by 5.88% and 13.18% on the MAPE index, respectively. Please refer to the data in Table 2 below. Furthermore, the method of using MBI to train the point prediction model and forming prediction intervals according to forecast error is faster than the method of using a meta-heuristic algorithm to train an interval prediction model. Generally speaking, the steady enhancement of PI quality is at the consumption of time. In the index of RMSE, DK-HTAN is improved by 17.84% and 21.90% over AGCLSTM and STALSTM models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Region**} & \multirow{2}{*}{**Model**} & **PICP, Flood** & \multirow{2}{*}{\begin{tabular}{c} **PICP,** \\ **Season** \\ \end{tabular} } & **Non-Flood** & \multirow{2}{*}{\
\begin{tabular}{c} **PINRW,** \\ **Flood** \\ **Season** \\ \end{tabular} } & **PINRW,** & \multirow{2}{*}{
\begin{tabular}{c} **PINRW,** \\ **Non-Flood** \\ **Season** \\ \end{tabular} } \\  & SVR & 0.689 (0.361) & 0.651 (0.233) & 0.237 (0.119) & 0.241 (0.012) \\  & RNN & 0.724 (0.412) & 0.706 (0.246) & 0.205 (0.145) & 0.198 (0.023) \\  & LSTM & 0.799 (0.698) & 0.755 (0.259) & 0.189 (0.189) & 0.194 (0.012) \\  & STALSTM & 0.812 (0.634) & 0.791 (0.312) & 0.177 (0.259) & 0.179 (0.031) \\  & AGCLSTM & 0.841 (0.287) & 0.820 (0.189) & 0.164 (0.012) & 0.172 (0.022) \\  & DKHTAN & 0.904 (0.264) & 0.896 (0.174) & 0.157 (0.014) & 0.163 (0.027) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance display data of the model under various interval prediction indicators.  
Figure 8: Station distribution map of Qijiang River Basin.