## 1 Introduction
The study indicated that the fluctuating pressure at the same position gradually increases with increasing gate openings, and the primary energy distribution occurs within the range of 10 Hz, with a dominant frequency below 1 Hz. [PERSON] [15] proposed a method to mitigate the flow-induced vibration of a radial shuice gate to ensure operational safety and long-term stability. Simulation analysis demonstrated that the vertical vibration acceleration of the gate decreased by 70% after attaching the supplementary plate. [PERSON] et al. [16] utilized a combined physical and numerical method to study the pressure characteristics of the radial gate plane at the bottom outlet of the Shahryar Dam in Iran. The measurements revealed that the pressure fluctuations in the current scenario were random in the time domain. Furthermore, the results from the hydraulic model experiment indicated that the fundamental frequency of the service radial gate was sufficiently far from the dominant frequencies in the power spectral density of the fluctuating pressure, thus mitigating the risk of resonance for the gate. The hydrodynamic pressure exerted on the radial gate panel significantly affects the gate's safe operation. [PERSON] et al. [17] developed a semi-empirical and semi-theoretical formula for hydrodynamic pressure acting on the radial gate panel, taking into account the characteristics of hydrodynamic pressure and considering the influence of hinge position and rotating arm radius on panel hydrodynamic pressure. In the context of partial opening conditions, the method for calculating panel hydrodynamic pressure for radial gates primarily relies on the principle of potential flow. Currently, there have been few studies conducted on the theoretical calculation of hydrodynamic pressure for radial gates. These studies are typically based on the principles of hydrostatic pressure and potential energy, supplemented by a hydrodynamic pressure function [18]. Theoretical calculations often fail to accurately predict turbulence intensity during gate discharge due to the inherent turbulence in real-world situations. Consequently, it becomes challenging to determine the hydrodynamic pressure characteristics of radial gates, such as fluctuating pressure and other variations, leading to lower theoretical calculation accuracy. As a result, only conservative pressure values can be obtained, limiting the study of gate stability and potentially leading to unsuitable material and structural designs. In addition, the turbulent nature of water flow in the discharge process of hydraulic radial gates poses challenges for direct simulation and calculation. With the emergence of artificial intelligence in various fields, data-driven machine learning techniques have become a focal point in turbulence research, attracting numerous experts and scholars to investigate. [PERSON] et al. [19] employed machine learning techniques to predict wind pressures around circular cylinders. The results suggested that gradient-boosting regression tree models offer an efficient and economical alternative to traditional wind tunnel tests and computational fluid dynamics simulations for determining wind pressures around two-dimensional smooth circular cylinders. [PERSON] et al. [20] utilized machine learning techniques to concentrate on modeling the fluctuating pressure rate of strain tensor in turbulent flows and quantifying uncertainties in the Reynolds stress model. Currently, fluctuating signals (such as fluctuating pressure, vibration acceleration, vibration stress, etc.) are often predicted directly from the original time series of the signal. This method does not thoroughly investigate the formation mechanism of fluctuating signals, and the accuracy needs further improvement. Therefore, it is necessary to perform decomposition preprocessing of the fluctuating signals before prediction.