## 1 Introduction  
Pipelines play a key role in transporting natural gas and corrosion within them is a serious problem with extensive impacts [1, 2]. In the economic aspect, corrosion causes significant direct and indirect losses, including the costs of repair and replacement, as well as the disruption of energy supply, leading to substantial financial losses. Regarding environmental concerns, oil and gas leaks cause pollution which can harm ecosystems for a long time and add to climate change by emitting greenhouse gases. When it comes to personal safety, corrosion makes pipeline structures weaker during oil and gas transport, which may trigger explosions and fires. Thus, it poses a great danger to the lives of workers and nearby people. Effective corrosion control is very important; it helps ensure safety, lengthen pipeline life, cut economic losses, and lower carbon footprints. An accurate prediction of corrosion rates matters a lot for effective control as it enables us to quickly understand and handle corrosion trends [3]. In practical production, many elements limit the precise prediction of natural gas pipeline corrosion: there are various influencing factors, noise, and nonlinear data traits and all of these factors can make predictions less accurate. To get better predictions, different research approaches have been used in many industries [4; 5; 6; 7]. Prediction models can be mainly divided into statistical ones, empirical ones, finite element ones, and machine learning ones. The division depends on how data are processed. Statistical models mainly rely on historical data and empirical formulas to establish prediction models. For instance, [PERSON] et al. introduced a mathematical statistics method for pipeline corrosion depth prediction. The results found this method has a high accuracy and can effectively guide the evaluation of the residual strength of oil and gas pipelines [8]. [PERSON] et al. developed a corrosion failure prediction model of a pipeline. The results showed that the method can accurately predict the probability of pipeline failure, guiding the development of corrosion control measures [9]. [PERSON] et al. proposed a probabilistic statistical method for predicting corrosion of gathering and transportation pipelines and demonstrated this method can predict the corrosion trend of a gathering and transportation pipeline well and provide a reliable basis for predicting the remaining life of the pipeline [10]. Additionally, numerous other statistical techniques have been commonly employed in corrosion prediction [11; 12]. Nevertheless, statistical models have limitations such as strong data dependence and poor extrapolation characteristics. Moreover, they are not suitable for data with noise and significant fluctuations. Empirical models are mathematical models that established based on industry experience and past corrosion data, used to estimate the remaining life and corrosion rates of oil and gas pipelines. Due to the vast amount of actual inspection and corrosion data results accumulated in the oil and gas industry, these models are fast, simple, and suitable for preliminary prediction and corrosion assessment. For example, [PERSON] et al. used computational fluid dynamics combined with NORSOK M-506 to predict CO\({}_{2}\) corrosion at the outlet header of a natural gas dehydration unit's air-cooled heat exchanger in a refinery. The study showed that the prediction results were in good agreement with field observations and the method successfully predicted the regional corrosion rates and identified the locations [13]. [PERSON] et al., addressing the issue of corrosion perforation in wet gas pipelines, optimized the De Waard 95 model using linear fitting to establish a new corrosion prediction model, W22. The study showed that, compared to the De Waard 95 model, the W22 model reduced the average error by 81.32%, indicating its high accuracy in predicting wet gas pipeline corrosion [14]. [PERSON] et al.