## 1 Introduction  
Flooding is an extreme weather event that poses a major threat to human life and property. Flooding can cause damage to houses, inundation of farmlands, damage to infrastructure, and even casualties, thereby seriously affecting lives and social stability. Therefore, the importance of water level calculation is obvious. By accurately simulating changes in the water level through water level calculation models, necessary precautions can be taken in advance, including the evacuation of people, reinforcement of buildings, and movement of valuable materials, which can minimize disaster damage [1; 2]. Traditional water level calculation models can be classified into empirical, statistical, and physical models. Empirical models are mainly based on historical observational data and empirical formulas. The relationship between hydrological variables (such as rainfall, runoff, and water level) is established using statistical methods that are applicable to the simulation of simple hydrological systems or lack observational data, such as the unit hydrograph method and the empirical rainfall-runoff (RR) model. Statistical models use time-series analysis,regression analysis, and other methods to analyze historical observations and establish mathematical relationships between hydrological variables to simulate future hydrological variables, such as the Autoregressive Moving Average and Autoregressive Integrated Moving Average models [3]. Physical models describe the dynamics of hydrological systems based on the physical principles of hydrological processes and partial differential equations, such as RR and distributed hydrological models. Physical models consider the interactions and spatial distribution between surface and subsurface hydrological processes and are suitable for complex hydrological systems and long-term simulations, including SWAT, HEC-HMS, and MIKE [4]. The river network in the eastern plains of China is criscrossed and the terrain is relatively gentle, resulting in a complex system of reciprocating water flow. The simulation of the water level depends on physical models, among which the MIKE model is widely used because of its strong applicability [5; 6; 7]. Physical mechanism models have a wide range of applications in the field of water level simulation; however, they have some shortcomings. First, the physical mechanism model has numerous setup parameters and model calibration requires accurate data, abundant professional knowledge, and experience. Second, the physical mechanism model may have limitations in dealing with complex hydrological processes, such as nonlinear and non-smooth processes, and accurately capturing dynamic changes in complex hydrological systems is difficult. In addition, the physical mechanism model requires long duration and computational resources for simulation, limiting its application in real-time simulations and decision support. Thus, the machine learning (ML) models are superior. ML models can automatically learn complex hydrological system characteristics and patterns using a large amount of data without relying on physical equations and complex parameter settings. ML models can handle complex hydrological processes, such as nonlinear and non-smooth processes, and adapt to hydrological data at different temporal and spatial scales. In addition, ML models have faster model training and simulation speeds and can realize real-time water level simulation and decision support. ML models have a large development potential in the field of water level simulation and can provide more accurate and efficient solutions for water level simulation and water resource management [8; 9]. ML is one of the fastest-growing technology fields, integrating multiple disciplines, such as computer science and statistics, and solving the problem of automatically building improved computational models through experience, which is the core of artificial intelligence and data science [10].