## 1 Introduction
; (2) the empirical intelligence model, based on prediction methods such as the genetic algorithm, support vector machine, neural network, ant colony optimization, etc. ; (3) the cluster intelligence combination model, based on multi-model mixing [16; 17]. At present, the combination model is a hot spot in the research of water quality prediction. Scholars have carried out much research on this topic, and have put forward a variety of combination prediction methods. [PERSON] et al. [18] used Monte Carlo simulation (MCS) to assess the risk of COD pollution in a river section, and introduced an artificial neural network (ANN) to improve the computational efficiency of the traditional method. [PERSON] et al. [19] used a combination of the ANN and multivariate linear Regression (MLR) methods to predict the suitability of groundwater quality data, and experiments showed that the ANN model outperformed the traditional MLR model. [PERSON] et al. [20] attempted to use the model to predict the Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD) in the wastewater water body by finding the optimized topological model of ANN and achieved a better prediction result. However, the water quality prediction model mentioned above has problems, such as the uncertainty of preset parameters, single model, and no preprocessing of data. [PERSON] et al. [21] used principal component regression (PCR) to extract the water quality index (WQI), and then fused it with gradient-enhanced regression (GBR) to form the PCR-GBR regressor, which provides a new idea for the prediction of water quality parameters. [PERSON] et al. [22] combined wavelet transforms (WT) with multiple machine learning models to explore the feasibility of the hybrid models for water quality dissolved oxygen prediction, and concluded that the multicomponent framework improved the prediction accuracy of the stand-alonemodel to a certain extent. [PERSON] et al. [23] introduced sparse self-encoder (SAE) on the basis of a long-short-story memory neural network (LSTM) to compose the SAE-LSTM model; the model pre-training by SAE was completed to initialize the network parameters, and satisfactory prediction results were achieved. [PERSON] et al. [24] proved the feasibility of the GRU model in water quality prediction by introducing the gated recurrent unit (GRU) model and comparing it with the LSTM and the recurrent neural network (RNN) model. [PERSON] et al. [25] introduced the Pathfinder Algorithm (PFA) for seeking parameter optimization of the GRU and combined it with the PCA algorithm to form the PCA-PFA-GRU model, which achieved satisfactory accuracy in the prediction of water quality dissolved oxygen. [PERSON] et al. [8] improved the Sparrow Search Algorithm (SSA) through the introduction of Skew-Tent mapping and combined it with the Support Vector Machines (SVM) model and the ISSA-SVR model was finally formed for the study of water quality prediction models. [PERSON] et al. [26] introduced complete empirical modal decomposition (CEEMDAN-LZC) to decompose the dissolved oxygen time series, reconstructed the new features, and then used the particle swarm optimization algorithm (PSO) to optimize the combination of the GRU model, which provided a reliable prediction of the DO content in aquaculture support. The above study shows that the combined model has the advantages of mining more useful information and improving the convergence speed and prediction accuracy, which provides a new direction for the prediction of the DO content in water quality, but the method of determining the combined weight coefficients and the optimization method need to be further studied.