of seemingly random phenomena is not the result of a single influencing factor; rather, it is produced by the combined influence of multiple factors within an entire system. The development of chaos theory, along with its ongoing expansion and application in environmental science [5; 6], has provided new methodologies for predicting river water quality [7; 8]. [PERSON] et al. analyzed dissolved oxygen (DO) levels in water bodies using chaos theory and the concept of phase space reconstruction [9]. They investigated the underlying evolutionary patterns of complex systems from a macroscopic perspective, decoupled and simplified the intricate relationships in water quality into a univariate system, and successfully made short-term predictions of water quality using a chaotic phase space model. Statistical methods have evolved since the beginning of the last century. The DO-BOD coupled water quality model, proposed by [PERSON] and [PERSON] in 1925, is considered the earliest statistical approach for water quality prediction modeling [10]. Another notable model is the ARIMA model, which stands for Autoregressive Integrated Moving Average Model. This model was proposed by [PERSON] and [PERSON] for time-series forecasting in the early 1970s. However, the method described in the text has certain limitations. It requires a large amount of data and is unable to handle nonlinear trends and multivariate elements, and the requirement for the timing data is that the data should be stationary or exhibit stability through differencing. Other statistical models, such as the Soil and Water Assessment Tool (SWAT) model and the Hydrological Simulation Pro-gram-Fortran (HSPF) model [11; 12; 13], also typically rely on a substantial amount of measured data for support and involve complex computational processes with relatively low simulation accuracy [14; 15]. In recent years, the utilization of machine learning algorithms has become prevalent in the domain of water quality prediction [16]. On the one hand, neural network models exhibit strong nonlinear expression capabilities. On the other hand, this is primarily attributed to their inherent benefits in adaptivity. Nevertheless, due to the escalating volume of data, the conventional neural network architecture proves to be inadequate for handling highly intricate datasets [17; 18]. Consequently, its performance capability diminishes significantly when confronted with extensive water quality data. In a study conducted by [PERSON] et al. [19], a simplified Convolutional Neural Network (CNN) was employed to forecast the concentration of DO. The performance of the CNN model was compared with that of the BPNN network model, and it was demonstrated that the CNN exhibited superior effectiveness and stability in predicting water quality. Meanwhile, other researchers have proposed the utilization of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) algorithms [20; 21]. These models have the ability to extract time-series features related to water quality and make predictions [22]. The LSTM algorithm has been shown to effectively predict water quality data with long time-series characteristics [23; 24]. However, this method requires more data [25], and the obtained results are unsatisfactory, lacking parallelism and exhibiting poor landing execution. Therefore, [PERSON] [26] conducted a search for the dependency relationship between water quality data using multiple time scales. Additionally, [PERSON] et al. utilized BiLSTM's robust capacity to capture and store information for forecasting water quality [27]. The findings of his research also confirmed the effectiveness of the BiLSTM model in extracting water quality information. The experimental findings demonstrated that the BiLSTM model outperformed both the LSTM model and other conventional models in terms of prediction accuracy, especially in the field of water quality prediction.