## 1 Introduction
Additionally, it is essential to refine and optimize the standard machine-learning model to bolster its predictive precision [14]. Consequently, to further elevate the predictive performance, both the algorithms and models necessitate enhancements and advancements. [PERSON] et al. amalgamated the traditional fireworks algorithm with the quantum optimization algorithm, significantly enhancing the efficacy of the optimization search. This methodology was employed in conjunction with a Support Vector Machine (SVM) to construct an advanced prediction model. Analytical verification has confirmed that this method markedly enhances both accuracy and solution speed [15]. [PERSON] et al. introduced a novel similarity-based weighted SVR model. Parameters were optimized based on varying sample weights using a hybrid swarm intelligence optimization \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Prediction Model** & **Principle** & **Advantages** & **Drawbacks** \\ \hline \multirow{3}{*}{Physical Model} & A mathematical model based on the physical process of water & Closer to the actual state of ice & Challenges in parameter acquisition; limited generalizability and theoretical reliability. & \\  & transiting from liquid to solid. & theoretical reliability. & practical utility. \\ \hline \multirow{3}{*}{Statistical Model} & Processing historical data through & Exhibits strong reproducible fit to & \\  & mathematical and statistical & historical data; & Highly data-dependent; computationally difficult. \\  & techniques to identify key factors & high prediction accuracy. & \\  & and model the thickness of icing. & & \\ \hline \multirow{3}{*}{Machine Learning Model} & Integrates machine learning for & Minimal-data requirements; rapid & The selection of model parameters can \\  & predictive analysis, bypassing the & predictive capabilities; & be subjective, impacting the \\  & physical processes of icing. & high accuracy. & prediction outcomes. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Transmission line icing prediction model. algorithm, combining PSO and the ant colony algorithm, thereby enhancing the model's generalization capacity, as evidenced by experimental results [16]. The incorporation of Bayesian optimization into SVR facilitates automatic parameter adjustment, taking into full account the inter-related [17; 18]. In the practical deployment of prediction models hinged on icing mechanisms, the accuracy is invariably contingent upon the judicious selection of parameters. Nevertheless, the icing process is characterizable as a chaotic time series, exhibiting considerable complexity and non-linearity alongside inherent inertia. Consequently, employing LSSVR to delineate the relationship between icing thickness and environmental factors not only maximizes the extraction of pertinent information from these factors but also minimizes data redundancy concerning ice cover thickness, thereby enhancing both the accuracy and efficiency of the prediction model. In the realm of meteorological data preprocessing, [PERSON] et al. proffered an innovative method designed for the handling of extended temporal sequences. This pioneering approach stratifies the dataset into two distinct categories: the harmoniously evolving smooth series and the oscillatory counterparts, serving as a predictive tool, thus imbuing the dataset with heightened resilience and interpretative clarity [19]. In pursuit of ascertaining the quintessential configuration for a hybrid wave wind farm, [PERSON] and co-authors introduced a pioneering wave wake preprocessing modality. This innovative paradigm orchestrates the seamless alignment of every geographic coordinate with a preconceived wave wake through a judicious amalgamation of recursive optimization and genetic algorithms, subsequently culminating in the comprehensive imputation of any missing data facets [20]. In 2023, [PERSON] et al.