## 3 Results and Discussion
### Water Level
#### 3.2.3 Restoration of Abnormal Water Level  
According to the class of abnormal data on the water level, the prediction method is used to restore it. The min/max method is used to normalize the data. All features of the dataset are used to train models. The algorithm still uses MLR, ANN, SVR, RF, and GBDT. The prediction model is as follows:  
\[Y_{pred}=g(w_{1},w_{2},q_{1},q_{2},v_{1},v_{2})\]  
where \(Y_{pred}\) is the predicted value of the high water level; \(g(. )\) is the prediction algorithm; \(w_{1},w_{2}\) are the water level nearby nodes; \(q_{1},q_{2}\) are the flow rate nearby nodes; and \(v_{1},v_{2}\) are the opening degree nearby nodes. The normal data in the data set are used for training, of which 30% is used as the testing set to evaluate algorithms. All algorithms use default parameters; Python 3.7 and the Scikit-learn platform are used to perform models. 1. Class 1 There are a total of 84 samples in class 1, among which 2 samples have abnormal water level data. In the class 1 scenario, the performance comparison between the training set and the test set is shown in Tables 3 and 4. 2. Class 2 Class 2 Class 2 Class 32 has 394 samples, among which 3 samples have abnormal water level data. In the class 2 scenario, the performance comparison between the training set and the test set is shown in Tables 5 and 6. 3. Class 3  
From the evaluation results, it can be seen that no matter the class of prediction model, the accuracy of the GBDT and RF is the highest, and GBDT has the best performance. However, MLR cannot learn the relatively complex nonlinear relationship well, so the accuracy is slightly worse. SVR and ANN perform poorly when the default parameters are used because they are sensitive to the algorithm parameters. Because the water level data are concentrated within a small range, even if the algorithm uses the average value as the prediction result, there will still be a small prediction error. The prediction outcomes of various algorithms are displayed in Figures 11-13 to show the performance of the model in a more intuitive way. It can be seen that ANN performs the worst in all three classes, and the prediction results are basically irrelevant to the actual water level, so it cannot be applied to the restoration work of the water level. The results of the other four algorithms are better but the performance gap is large, although the errors are small as observed by the evaluation indicator. The best performance is still GBDT and RF, among which GBDT is the best, and the correlation between the actual water level and the predicted value in class 2 is even as high as 0.994. The next best performers are MLR and SVR, which correspond to the previous analysis. Therefore, GBDT with optimal accuracy and stronger robustness is used to restore the high water level data. The restored results are indicated in Table 9. According to the table, GBDT restores the abnormal data of each class, and the restored data are within the normal range and conform to the data distribution law of the respective class. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **MAE (Meter)** & **MAPE** & **RMSE (Meter)** \\ \hline RF & 0.0233 & 0.259\%\(\infty\) & 0.0498 \\ SVR & 0.2301 & 2.551\%\(\infty\) & 0.3429 \\ GBDT & 0.0293 & 0.326\%\(\infty\) & 0.0400 \\ MLR & 0.1568 & 1.740\%\(\infty\) & 0.2289 \\ ANN & 3.3886 & 37.677\%\(\infty\) & 4.6920 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Model evaluation on the class 3 water level training set. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **MAE (Meter)** & **MAPE** & **RMSE (Meter)** \\ \hline RF & 0.090 & 1.00\% & 0.215 \\ SVR & 0.237 & 2.62\%\(\infty\) & 0.344 \\ GBDT & 0.082 & 0.91\%\(\infty\) & 0.160 \\ MLR & 0.167 & 1.85\%\(\infty\) & 0.241 \\ ANN & 4.161 & 46.21\%\(\infty\) & 5.954 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Model evaluation on the class 3 water level testing set.