###### Abstract
In order to improve the accuracy of water quality prediction, [PERSON] et al. [41] utilized the VMD to reduce noise in the datasets of total nitrogen and total phosphorus gathered from four online monitoring stations surrounding a lake. Through the implementation of this approach, accurate final data regarding water quality parameters for every element are successfully anticipated. Their evaluation yielded advantageous outcomes with regards to the forecasting of water quality. According to the research presented above, to thoroughly analyze the patterns of change over time in water quality parameter data and enhance the precision of the forecasting model, this study focuses on DO, a crucial water quality parameter. A novel hybrid model, named VMD-GWO-GRU, is proposed to forecast water quality parameters by employing the VMD technique for data feature extraction and signal denoising, along with the GWO to upgrade the conventional GRU model. Unlike conventional neural networks, this approach addresses the issue of challenging parameter tuning, aiming to boost the forecasting accuracy of water quality parameters. The findings of this research will facilitate the forecasting of important water quality metrics and provide essential assistance to water treatment organizations in improving their capacity to observe and regulate water quality. The experiment scheme of this study can be seen in Figure 1. Initially, the raw data were processed in advance to obtain a dataset, which was then divided into K different modal components using the VMD technique, which are represented as intrinsic mode function (IMF)1 to IMF K in Figure 1. Each modal component was divided into a training dataset and a test dataset. The training dataset was used to train the model, and the test dataset was used to evaluate the effectiveness of the model. After that, a GRU neural network was built for each component, and the parameters of each GRU model were adjusted using the GWO algorithm during the training process. These models are represented as GWO-GRU1 to GWO-GRU K in Figure 1. Finally, the prediction results PRED 1 to PRED K of each part were superposed and calculated to obtain the final prediction result. This paper is structured in a systematic manner to provide a comprehensive overview of the study conducted. The sources of water quality parametric data acquisition are discussed in Section 2, along with the initial processing of the acquired data to ensure a quality dataset. Following this, Section 3 delves into the methodology and theoretical basis used in this study, offering a detailed description of the approach taken. Moving on to Section 4, the experimental procedure and results of the water quality parametric forecasting models are presented, accompanied by a comparison of the outcomes of each model. The effectiveness and reliability of the models were thus thoroughly analyzed and evaluated. Furthermore, Section 5 examines the limitations of the research presented in this paper, while also highlighting potential avenues for future research and development in this field. Lastly, in Section 6, a summary of the entire paper is provided, encapsulating the conclusions drawn from this study.