## 4 Results and Discussion
### Training and Validation of Machine Learning (ML) Models  
#### 4.2.1 Training of Machine Learning (ML) Models  
Owing to the limitations of the MIKE11 model in dividing the study area into seven sub-catchments and historical data, one station from each sub-catchment was selected for training the ML water level simulation model. The selected stations were Haiantong (sub-catchment 1), Banjing (sub-catchment 2), Jiaogangzha (sub-catchment 3), Yangkouwaizha (sub-catchment 4), Juegang (sub-catchment 5), Dingyan (sub-catchment 6), and Shigang (sub-catchment 7). The selected time was from 6 July 2023, at 1:00, to 10 July 2023, at 23:00. The input samples were divided into a training set (70%) and a testing set (30%) by the dividerand function [15; 35]. The results of the simulation and process simulation verification rate, peak simulation error, peak present time error, RMSE, and NSE were computed to obtain the final evaluation value of the model performance and compared with the MIKE11 model. The results are presented in Table 6 and Figures 5 and 6. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Process**} & \multicolumn{2}{c}{**Peak**} & \multirow{2}{*}{**Peak Present Time Error (h)**} & \multirow{2}{*}{**RMSE (m)**} & \multirow{2}{*}{**NSE**} \\  & & **Simulation** & & **Simulation** & & \\  & & **Verification Rate (\%)** & & & & \\ \hline \multirow{4}{*}{Haiantong} & SVM & 62.79 & 0.076 & 5.41 & 0.200 & 0.808 \\  & RF & 92.59 & 0.011 & 1.08 & 0.142 & 0.834 \\  & GBDT & 91.78 & 0.013 & 1.62 & 0.149 & 0.829 \\  & SVM & 72.46 & 0.142 & 25.95 & 0.184 & 0.818 \\ \multirow{4}{*}{Banjing} & RF & 93.39 & 0.005 & 1.08 & 0.147 & 0.826 \\  & GBDT & 90.97 & 0.006 & 1.62 & 0.146 & 0.826 \\  & SVM & 65.21 & 0.231 & 2.16 & 0.234 & 0.816 \\ \multirow{4}{*}{Jiaogangzha} & RF & 79.71 & 0.032 & 0.38 & 0.142 & 0.835 \\  & GBDT & 87.75 & 0.009 & 0.27 & 0.148 & 0.831 \\ \multirow{4}{*}{Yangkouwaizha} & SVM & 67.63 & 0.050 & 3.24 & 0.143 & 0.798 \\  & RF & 88.56 & 0.003 & 0.08 & 0.119 & 0.836 \\ \multirow{4}{*}{Juegang} & GBDT & 90.17 & 0.002 & 0.05 & 0.121 & 0.828 \\  & SVM & 74.87 & 0.025 & 2.16 & 0.130 & 0.829 \\ \multirow{4}{*}{Juegang} & RF & 93.39 & 0.001 & 0.03 & 0.107 & 0.832 \\  & GBDT & 88.56 & 0.002 & 0.06 & 0.104 & 0.835 \\ \multirow{4}{*}{Dingyan} & SVM & 64.41 & 0.173 & 3.24 & 0.172 & 0.827 \\  & RF & 85.34 & 0.009 & 4.33 & 0.133 & 0.848 \\ \multirow{4}{*}{Shigang} & GBDT & 80.51 & 0.011 & 4.33 & 0.132 & 0.849 \\  & SVM & 79.71 & 0.034 & 5.41 & 0.105 & 0.831 \\ \multirow{4}{*}{Shigang} & RF & 93.39 & 0.001 & 1.08 & 0.085 & 0.842 \\  & GBDT & 93.39 & 0.001 & 1.08 & 0.081 & 0.845 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of calibration results for machine learning models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multicolumn{2}{c}{**Process**} & \multirow{2}{*}{**Peak Simulation**} & \multirow{2}{*}{**Peak Present Time**} & \multirow{2}{*}{**RMSE (m)**} & \multirow{2}{*}{**NSE**} \\  & **Simulation** & & & & \\  & **Verification Rate** & & & & \\  & **(\%)** & & & & \\ \hline \multirow{2}{*}{Haiantong} & 100 & 0.02 & 5.17 & 0.106 & 0.973 \\ \multirow{2}{*}{Rutaiyunhezhadong} & 98.31 & 0.06 & 5.33 & 0.134 & 0.952 \\ \multirow{2}{*}{Banjing} & 100 & 0.097 & 4.42 & 0.116 & 0.964 \\ \multirow{2}{*}{Ruhaiyunhe} & 100 & 0.091 & 1.75 & 0.115 & 0.966 \\ \multirow{2}{*}{Jiaogangzha} & 87.29 & 0.1 & 0.42 & 0.167 & 0.927 \\ \multirow{2}{*}{Yangkouwaizha} & 92.37 & 0.045 & 0.08 & 0.159 & 0.878 \\ \multirow{2}{*}{Juegang} & 98.31 & 0.002 & 0.08 & 0.117 & 0.929 \\ \multirow{2}{*}{Jingyan} & 99.15 & 0.018 & 0.25 & 0.162 & 0.930 \\ \multirow{2}{*}{Jiuweigangzha} & 88.14 & 0.023 & 0.17 & 0.138 & 0.964 \\ \multirow{2}{*}{Shigang} & 93.22 & 0.073 & 12.33 & 0.276 & 0.353 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of calibration results for MIKE11 model.