## 1 Introduction
Recently, hydrological simulation practitioners have attempted to apply various ML methods to improve the accuracy of hydrological simulations. Various types of model improvements and application studies have achieved fruitful research results. The scope of ML is wide, and many types of algorithms can be categorized according to the learning method: supervised, unsupervised, semi-supervised, and reinforcement learning. Supervised ML approaches have obvious advantages, such as using labeled data for training, understanding, and interpreting models, accelerating model convergence, and evaluating model performance; therefore, in this study, the Support Vector Machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT) models were selected as supervised learning approaches. The SVM models are excellent in dealing with small samples and nonlinear and high-dimensional data, and they can efficiently deal with the complexity of feature spaces in the boundaries and have strong generalization ability. [PERSON] et al. [11] established the deep neural network (DNN), Elman neural network, and SVM runoff simulation models based on the historical runoff data of Huanglongtan and Pankou Reservoirs. The simulation results show that the above three models exhibit excellent performance in the medium- and long-term runoff simulation of Huanglongtan Reservoir, with high accuracy and few errors. Compared with the DNN and Elman neural network, the SVM model has a smaller error in flood peak simulation and higher simulation accuracy. The RF model is known for its parallel processing capability and robustness to missing data. It can handle large-scale datasets, effectively reduce the risk of overfitting, and is suitable for complex classification and regression problems. [PERSON] et al. [PERSON] et al. (2018, 2019) simulated streamflow at a lead time of 1 d in 86 watersheds in the Pacific Northwest based on the RF, naive, and multiple linear regression (MLR) models, and suggested that the RF model performs better in snowmelt-driven watersheds than in rainfall-driven watersheds. The largest improvements in the simulation compared with the benchmark models were observed among the rainfall-driven watersheds. The GBDT model improves the performance of the model by training the decision tree model iteratively and integrating multiple weak learners, which performs well in dealing with large-scale data and complex problems, and can deal with various data types, including numerical and categorical data. [PERSON] (2018) used the monthly runoff sequences of six hydrological stations in the main stream of the Yellow River as the research object and constructed an RF model based on the bagging integrated learning strategy. Additionally, GBDT, Extreme Gradient Boosting, and Light Gradient Boosting Machine models were constructed based on the boosting integrated learning strategy to comprehensively capture the change characteristics of the runoff sequence. The results showed that the simulation results of the four integrated learning models in the training period generally met the simulation accuracy requirements, whereas the simulation results in the test period had different degrees of error. The SVM, RF, and GBDT models have unique advantages in different application scenarios and can provide efficient and accurate solutions for ML tasks, such as water level simulation ([PERSON] et al., 2018). The current application of ML models in water level simulation focuses on water level simulation for reservoirs, lakes, and rivers, and remains at an early stage. Although ML has achieved some results in water level simulation, some aspects require further research. Overall, this study consists of three innovations. First, rainfall, water level, and water conservancy hub scheduling strategies were considered simultaneously in the water level simulation of the plain river network area. Second, three ML models were applied to the water level simulation in the plain river network area, and the performance of the different ML models was compared.