## 1 Introduction  
In recent years, corrosion in pipelines, as an important factor leading to pipeline failure, has been prevalent in the oil and gas, chemical, and urban rainwater and sewage treatment industries, seriously threatening the stable operation of pipeline networks. The frequent occurrence of accidents has caused huge economic losses, resource wastage, and even ecological pollution to the pipeline transportation industry [1, 2]. Typical cases of pipeline corrosion-induced failure are illustrated in Figure 1, which visually demonstrates the severity and impact of such incidents. In the urban drainage pipeline system, all kinds of accidents caused by corrosion increase significantly with the growth of pipeline service time. Therefore, the accurate prediction and scientific assessment of pipeline corrosion rate to guide the preventive maintenance and optimization of pipeline management strategy is of far-reaching theoretical significance and an urgent practical need in order to enhance system efficiency, investigate pipeline safety hazards, and ensure stable operation of pipelines [3, 4, 5]. Literature searches have found that current pipeline corrosion prediction studies are mainly categorized into model-driven methods and data-driven methods. The core of the model-driven approach is to analyze the physicochemical changes in the corrosion reaction process, and such methods obtain the key parameters of the pipeline through field inspection, laboratory simulation, and theoretical derivation, and construct a quantitative relationship model between the corrosion behavior and these parameters. [PERSON] et al. [6] discussed the corrosion mechanism of submarine pipelines under a CO\({}_{2}\) environment, systematically summarized the multiple factors affecting the corrosion rate, and introduced in detail the control steps and boundary conditions of the principal modeling and empirical modeling methods in corrosion assessment and prediction. [PERSON] et al. [7] studied the internal corrosion law of a concrete drainage pipe, compared the prediction effect of multiple models, and evaluated the corrosion depth of the pipe through numerical simulation. Although the model-driven methods show good generality and interpretability by virtue of the solid physicochemical theoretical foundation, the strict condition assumptions are often difficult to meet in the face of the actual scenarios of complex pipeline corrosion causes, and it is difficult to determine the periodicity, resulting in the limited application of the model [8; 9]. With the development of monitoring technology and data accumulation, data-driven methods have gradually become a new direction in the field of pipeline corrosion prediction. These methods establish predictive models by mining the intrinsic relationships and development patterns of large amounts of actual monitoring data, providing new ideas to overcome the limitations of traditional methods. The data-driven methods can be summarized into three main categories: mathematical statistics methods, machine learning, and deep learning. In terms of mathematical statistics methods, [PERSON] et al. [10] established  
Figure 1: Examples of pipeline corrosion failure. two models, GM (0, 6) and GM (1, 6), based on gray system theory to predict and analyze the corrosion rate data of submarine pipelines. [PERSON] et al. [11] took a certain sea area seabed pipeline as an example, preprocessed the data using principal component analysis (PCA), extracted the main factors affecting pipeline corrosion, applied project pursuit regression (PPR) to establish a mathematical model for corrosion prediction, and optimized the model using beetle antennae search (BAS). MATLAB simulation and comparison with actual values showed that the model has higher prediction accuracy. Against the backdrop of continuous advancements in technology, machine learning, as a key branch of the artificial intelligence system, has gained significant attention in both academic and applied fields [12].