## 1 Introduction
introduced a CO\({}_{2}\) corrosion rate correction factor to the semi-empirical model DWM, establishing a CO\({}_{2}\) corrosion rate prediction model suitable for the Bohai Sea region which was verified for reliability through comparative analysis [15]. However, empirical models typically require a large amount of detailed data on materials, process conditions, and environments and they have a strong dependence on data, limiting their applicability. Finite element models mainly utilize CFD simulations to study the effects of fluid corrosion and erosion corrosion as fluids flow past the inner walls of pipelines. They are useful for analyzing the impact of high-velocity areas within pipelines on corrosion and are suitable for predicting localized corrosion characteristics and corrosion in special pipeline structures. For instance, [PERSON] et al. used the finite element method to study life prediction in an H\({}_{2}\)S/CO\({}_{2}\) coexisting deep well environment, demonstrating the accuracy of the established model [16]. [PERSON] et al. developed a method based on ABAQUS for predicting the ultimate load of X80 pipelines with defects which was compared with actual test data of X80 pipelines, showing good applicability and high calculation accuracy [17]. [PERSON] et al. proposed a finite element model of a certain type of vehicle-mounted compressed natural gas cylinder using ANSYS, studying the corresponding relationship between remaining life and corrosion defects throughout the cylinder's lifecycle and found that this relationship could be accurately predicted. Finite element analysis excels in analyzing localized corrosion and corrosion in specific areas but requires high technical expertise and is computationally expensive [18]. With increasingly sophisticated computational capabilities and data collection technologies, AI (Artificial Intelligence) methods have been commonly applied in prediction field. Among these, Artificial Neural Networks (ANNs) [19] and Support Vector Machines (SVMs) [20] are the most popular. Back propagation neural networks (BPNNs) are a representative method of Artificial Neural Networks known for their outstanding predictive performance, which is closely related to their strong fault tolerance and robustness. Additionally, BPNNs are not constrained by complex mathematical formulas and quantitative correlations. Therefore, BPNNs have been widely used in many areas of corrosion prediction [21]. However, BPNNs are prone to overfitting and getting trapped in local minima. In recent years, many studies have shown that various optimization algorithms are effective in improving predictive performance and solving over-fitting. Consequently, excellent population-based optimization algorithms such as particle swarm optimization (PSO) [22; 23], Genetic Algorithms (GAs) [24], Differential Evolution (DE) [25], Simulated Annealing (SA) [26], and Artificial Bee Colony (ABC) models [27] have been adopted to determine the parameters of BPNNs, thereby improving prediction accuracy. However, the original data often contain noise interference, redundancy, and non-linearity, due to which these methods may fail to accurately capture the main features of the initial data, leading to unsatisfactory prediction results. To address the non-linear and non-stationary issues of original data series, data decomposition techniques are adopted to capture the main features of the original data series, enhancing the model's predictive performance [28; 29]. The statistical results of application cases for different types of prediction models are shown in Table 1. The analysis of the above literature reveals that hybrid models incorporating decomposition techniques and optimization algorithms have better predictive performance.