###### Abstract
employed an improved whale optimization algorithm (IWOA) for the optimization of the GRU model in order to provide a reference for modeling water quality prediction models [27]. The IWOA-GRU was then compared with other prediction models, including the random forest (RF) model, RNN model, CNN model, and LSTM model. The findings of their experiment demonstrated that the model's predictive accuracy and generalization capabilities surpassed those of the comparison models. Among the above-mentioned optimization algorithms, the improved PSO algorithm emulates the foraging habits of a bird flock, considering each candidate solution as a particle. This algorithm features a straightforward structure and a rapid convergence rate, making it suitable for real-time applications. Nonetheless, with continuous iterations, particles may assimilate too much, thereby reducing their exploratory abilities. On the other hand, the IWOA algorithm models the search process based on the predatory behavior of whales and incorporates dynamic adjustment parameters to boost search efficacy. While this algorithm excels in high-dimensional and complex search spaces, it is computationally intensive and challenging to implement. The grey wolf optimizer (GWO) algorithm has been a focal point of interest among researchers in recent years because of its impressive convergence capabilities, minimal parametric requirements, and straightforward implementation. Its effectiveness in optimizing complex problems has made it a popular choice for academics and professionals alike [28; 29]. In the field of water quality parametric prediction, the latest research results also include signal decomposition and noise reduction in unstable and noisy water quality parametric data to improve the accuracy of the established prediction model, especially the modal decomposition method for historical water quality parameters. After decomposing the data, a multi-modal component combination prediction model is established to significantly enhance the prediction effect [30; 31]. Currently, the widely used signal decomposition algorithms are the empirical mode decomposition (EMD) algorithm [32], principal component analysis [33], wavelet transform (WT), etc. [PERSON] et al. [34] combined a data preprocessing module based on EMD with a prediction model based on LSTM neural networks in order to improve the accuracy of their modeling approach. In order to address the limitations of existing EMD signal decomposition methods, which are prone to endpoint effects and modal overlap [35], as well as the challenges associated with WT, which involve selecting an appropriate decomposition scale, [PERSON] et al. proposed an EWT method that combines EMD and WT. The results verified the effectiveness of this method in removing noise and performed better than other denoising algorithms in locating different parts of abnormal biomedical signal data [36]. Researchers have also completed a lot of work to improve EMD. [PERSON] et al. used ensemble empirical mode decomposition (EEMD) combined with the ARIMA method to establish a drought forecasting model, and their results showed that using the EEMD-ARIMA model significantly improved the accuracy of their drought forecast [37]. [PERSON] et al. used CEEMD combined with the LSTM method to establish a prediction model for the DO indicator of water pollution [38]. The introduction of the CEEMDAN method increased their modeling accuracy by 35%. On the other hand, a variational mode decomposition (VMD) method that can also solve the shortcomings of EMD was proposed by [PERSON] et al. The VMD is distinguished by its quasi-orthogonality, non-recursive properties, and strong auto-adaptive capability [39]. [PERSON] et al. introduced a technique for spectral sample generation utilizing variational mode decomposition in conjunction with a generative adversarial network [40], and the method's efficiency was assessed by constructing a regression model and forecasting the COD in real water samples.