## 1 Introduction
GW excels at long-range detection of large-scale structural defects like wall thinning, cracks, and weld anomalies, but its lower resolution limits its sensitivity to smaller, localized defects, such as pitting corrosion and micro-cracks [27]. Metal Magnetic Memory (MMM) detects early-stage stress and strain in ferromagnetic materials by identifying magnetic field distortions caused by structural anomalies. MMM is advantageous for inspecting buried pipelines and inaccessible locations but is restricted to ferromagnetic materials and is vulnerable to interference from external magnetic fields,which can be common in mining environments [28; 29; 30]. The maximum effective depth depends on factors such as the pipe's material and diameter, the magnetometer's sensitivity. Generally, MMM is most effective for shallowly buried pipelines (typically less than a few meters). Soil composition, moisture, and the presence of magnetic minerals can create background noise and distort the magnetic field, making it difficult to isolate signals from the pipeline. Magnetic field scattering at the interface between the pipe and the soil, as well as the presence of rocks or other nearby metallic debris, can also interfere with measurements. In-Line Inspection (ILI), or \"pigging\", involves devices that travel through pipelines, providing high-resolution data on internal corrosion, cracks, and deformations. ILI offers comprehensive data [31] and requires pipelines to be compatible with pigging devices, which can limit its use in pipelines with non-standard structures [8]. ILI may also require temporary flow adjustments [23; 32; 33], while modern ILI systems equipped with speed control units can operate without significant disruption to pipeline operations. Current advances in ILI pipeline inspection and corrosion detection technologies highlighting diverse non-destructive testing (NDT) methods and multi-sensor systems tailored for complex infrastructure. The work by [PERSON] et al. [23] reviews intelligent pipeline inspection gauges (PIGs) that utilize sensor fusion technologies, including micro-inertial measurement units (MIMUs), to improve the accuracy and efficiency of pipeline surveying. This approach integrates multiple sensors for a comprehensive assessment of pipeline integrity, which is critical for detecting and localizing defects in buried pipelines that are often missed by conventional methods. Novel nondestructive techniques, such as remote field eddy current [34], ground-penetrating radar [7; 9], and advanced acoustic emission methods [20], offer significant advantages for pipeline health monitoring and corrosion detection. These technologies, as demonstrated by recent studies, enable high-accuracy defect identification and real-time pipeline condition assessments, particularly in environments where traditional methods fall short due to accessibility constraints [20; 34]. Prediction capabilities in multi-regional water networks demonstrated importance of integrating machine learning with conventional inspection methods to address complex and large-scale pipeline systems effectively [6; 35; 36]. Furthermore, knowledge graphs and neural networks have demonstrated efficacy in predicting corrosion rates within oil and gas pipelines [37]. A review by [PERSON] et al. [38] underscores the importance of accurate predictive analytics within the oil and gas sector, highlighting the application of machine learning for this purpose. This review provides an overview of recent research, detailing various methodologies, their advantages, and disadvantages, while also suggesting future research directions to enhance predictive analytics in the sector. However, the current literature offers limited guidance on technology selection for water systems, particularly within the mining industry. Although each pipeline inspection technology offers distinct advantages, selecting the most appropriate method for the diverse challenges of mining environments remains a complex task.