## 5 Conclusions  
In this study, the Yangtze River Delta Plain river network area (JiaoGang Basin) was used as the research object. A physical mechanism model was established based on the MIKE software, ML models were established based on the SVM, RF, and GBDT algorithms, and the water level in this area was simulated using the four models and compared.  
1. By coupling hydrodynamic and hydrological models for calibration and validation, the physical mechanism model derived a set of model parameters suitable for the characteristics of the region. The process simulation verification rate for most stations exceeded 85%, with peak simulation errors all below 0.1 m and peak present time errors below the Allowable Errors. All other stations, except for Shigang station, had an RMSE of less than 0.2 and an NSE greater than 0.8. This model can better simulate the hydraulic characteristics of the river network in the JiaoGang Basin and can be applied to flood simulations, river network planning, water resource scheduling, and other projects in this area. Based on the physical mechanism model used in this study, more in-depth research can be conducted to provide an application basis and technical support for the development of the region.
2. The training and validation of ML water level simulation models showed that the applicability of the RF and GBDT models was higher than that of the SVM model. In the training and validation periods, the RF and GBDT models had an RMSE of less than 0.25 and an NSE greater than 0.7, and the three models had a certain degree of fluctuation. The fluctuation of the SVM model was larger than that of the RF and GBDT models, which is useful for informatization in the Yangtze River Delta Plain river network area.
3. Overall, the physical mechanism model and ML water level simulation models can better simulate the water level in the JiaoGang Basin. The RF and GBDT models are considerably better than the physical mechanism model in terms of the peak simulation errors and the peak present time errors. The fluctuation of the simulation results (RMSE and NSE) of the ML water level simulation models is less than those of the physical mechanism model. At Shigang station, the simulation accuracies (RMSE and NSE) of the ML water level simulation models are much higher than that of the physical mechanism model; the ML water level simulation models can save time costs [13]; and the physical mechanism model pays more attention to the details. The physical mechanism model and ML water level simulation models can be applied to the water level simulation of the Yangtze River Delta Plain river network area (JiaoGang Basin), which can provide a certain degree of support and help.  
**Author Contributions:** X.G.: writing--original draft, validation. Y.L.: writing--review and editing, formal analysis. C.G.: writing--review and editing, formal analysis. D.Q.: data curation, software. Q.W.: conceptualization, investigation. Y.C.: methodology, visualization. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially funded and supported by the Key R&D Program of Xinjiang Uygur Autonomous Region (Grant No. 2023B02044-2) and the Major Scientific and Technological Projects of the Ministry of Water Resources (Grant No. SKS-2022021).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors thank the editor and reviewers for their valuable comments.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.