## 3 Results
### Experimental Results
#### 3.4.2 Robustness Analysis  
The model based on a data-driven and knowledge-guided framework effectively utilizes prior information and domain knowledge. In theory, it has stronger feature extraction ability and lower training data requirements for flood time-series data compared to pure data-driven flood prediction models. In this section, we analyzed the performance of data-driven and knowledge-guided flood prediction models and flood prediction ANN models under different boundary conditions and observations through comparative experiments. Specifically, we evaluated the performance and robustness of different models under different noise levels and outlier ratios. Table 3 shows the impact of different noise levels on observation results compared to model predictions under different scales of noise. The noise gradually increased from 5% to 20%. Firstly, we can see that under different noise levels, DK has better PICP values compared to other models. This explains the performance advantages of the proposed model in interval prediction. Taking the Qijiang River Basin as an example, the PICP value of DK is about 15% higher than the dominant STALSTM. This may be because the model proposed in this article is based on data-driven and knowledge-guided design, and has strong robustness in the field of hydrological prediction. Of course, as the noise level increases, the interval performance of DK also shows a gradual convergence trend, which is also where we need further research and improvement. On the other hand, even if the noise level increases to 20%, the PICP performance of DK will not decrease much, which reflects the noise robustness of the model proposed in this paper with the help of data-driven and knowledge-guided techniques. Generally speaking, flood time-series data in practical engineering usually contain noise-interference data. It is difficult to distinguish useful data from interference values based on a single data-driven model. However, the dual driving mechanism proposed in this paper can effectively filter interference values using flood physical constraints, thereby improving the noise robustness of the DK model. For the comparison of different models in flood time series data prediction, Outlier usually have a greater impact on the prediction results than noise. Therefore, here we compare the PICP performance of different models under different outliers. As shown in Table 4, the performance comparison of DK under different outlier ratios guided by flood prediction and physical constraint theory is shown in Table 5. The PICP value of DK proposed in this paper is higher than that of other baseline models under various \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Region** & **Model** & **MAPE, Flood Season** & **MAPE, Season** & **RMSE, Flood Season** \\ \hline \multirow{5}{*}{Qijiang} & SVR & 0.158 (0.361) & 0.163 (0.233) & 0.337 (0.119) & 0.341 (0.012) \\  & RNN & 0.149 (0.412) & 0.154 (0.246) & 0.335 (0.145) & 0.338 (0.023) \\ \cline{1-1}  & LSTM & 0.135 (0.698) & 0.141 (0.259) & 0.306 (0.189) & 0.304 (0.012) \\ \cline{1-1}  & STALSTM & 0.129 (0.634) & 0.133 (0.312) & 0.283 (0.259) & 0.297 (0.031) \\ \cline{1-1}  & AGCLSTM & 0.119 (0.287) & 0.120 (0.189) & 0.269 (0.012) & 0.282 (0.022) \\ \cline{1-1}  & DK-HTAN & 0.112 (0.264) & 0.116 (0.174) & 0.221 (0.014) & 0.203 (0.027) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance display data of the model under MAPE and RMSE indicators.