## 4 Results and Discussion
### Training and Validation of Machine Learning (ML) Models
#### 4.2.1 Training of Machine Learning (ML) Models
Table 6 shows that the simulation results of the RF and GBDT models are superior to those of the SVM model; for example, at Haiantong station, the process simulation  
Figure 5: Comparison chart between simulated and observed water levels in machine learning models: (**a**) Haiantong, (**b**) Banjing, (**c**) Jiaogangzha, (**d**) Yangkouwaizha, (**e**) Juegang, (**f**) Dingyan, and (**g**) Shigang. Figure 6: Performance comparison bar chart between MIKE11 and machine learning models. verification rates of the SVM, RF, and GBDT models are 62.79, 92.59, and 91.78; the peak simulation errors are 0.076, 0.011, and 0.013; and the peak present time errors are 5.41, 1.08, and 1.62, respectively. In terms of the process simulation verification rate, the simulation results of the RF and GBDT models do not have considerable advantages compared with those of the MIKE11 model. For example, at Jiaogangzha station, the simulation results of the MIKE11, RF, and GBDT models are 87.29, 79.71, and 87.75, respectively. In terms of the peak simulation error and peak present time error, the simulation results of the RF and GBDT models are superior compared with those of the MIKE11 model. For example, at Banjing station, the peak simulation errors of the MIKE11, RF, and GBDT models are 0.097, 0.005, and 0.006 and the peak present time errors are 4.42, 1.08, and 1.62, respectively. The following can be derived from Figures 5 and 6:  
1. The ML water level simulation models are accurate for all stations; however, they fluctuate in the process of water level simulation. The SVM model has the most evident fluctuation in the process of water level simulation. The maximum RMSE is 0.234 at the Jiaogangzha station, the minimum NSE is 0.798 at the Yangkouwaizha station, the RMSEs of the three models are all less than 0.25, and NSE values exceed 0.75. For example, at the Haiantong station, the RMSEs of the SVM, RF, and GBDT models are 0.200, 0.142, and 0.149, respectively, and the NSE of the SVM, RF, and GBDT models are 0.808, 0.834, and 0.829, respectively, indicating that the GBDT and RF models are more accurate than the SVM model, and the GBDT and RF models have better performance [36]. The RMSE of the SVM model has a maximum difference of 0.129 between Jiaogangzha and Shigang stations, the RMSE of the RF model has a maximum difference of 0.062 between Nanjing and Shigang stations, and the RMSE of the GBDT model has a maximum difference of 0.068 between Haiantong and Shigang stations. The NSE of the SVM model has the largest difference of 0.033 between Shigang and Yangkouwaizha stations, the NSE of the RF model has the largest difference of 0.022 between Nanjing and Dingyan stations, and the NSE of the GBDT model has the largest difference of 0.023 between Nanjing and Dingyan stations, which indicates that the simulation results (RMSE and NSE) of the three models have a certain degree of fluctuation, and the fluctuation of the SVM model is larger than that of the RF and GBDT models. For example, [PERSON] et al. [37] brought out a combined forecast model based on Support Vector Machines (SVMs) and entropy weight and applied it to the Three Gorges Reservoir runoff prediction. Compared to the Nearest Neighbor Bootstrapping Regressive model (NNBR), Mean Generating Function (MGF), and Automatic Regressive model (AR), the combined model is better than the individual model. In [PERSON] et al. [38], based on multi-models (multiple regression (MR), random forest (RF), and convolutional neural network-gradient boosting decision tree (CNN-GBDT)), precipitation integration is carried out to forecast runoff, and the CNN-GBDT method is superior to the multiple regression method and random forest method in improving the precision of rainfall forecasts. 2.