Average Corrosion Rate Prediction Model for Buried Oil and Gas Pipelines Based on SSA-LightGBM - Weigang Fu; Haitao Wang; Kuankuan Zhang; Xia Wang; Kunlun Chen; Chunmei Sun; Zhengwei Wang; Liuyang Song; Niannian Wang (IEEE Access)
Corrosion represents a major cause of damage and leakage in oil and gas pipelines, making accurate corrosion rate prediction critical for operational safety. This study establishes an average corrosion rate prediction model using the Sparrow Search Algorithm-optimized Light Gradient Boosting Machine (SSA-LightGBM). To enhance model accuracy, Stochastic Perturbation Data Augmentation (SPDA) was applied to expand the original dataset from 60 to 300 samples. The optimized SSA-LightGBM model was then trained, and its performance was rigorously evaluated through error analysis and stability assessment. Results demonstrate that the SSA-LightGBM model achieves exceptional accuracy and stability, with an R2 value of 0.9813 and stability index (Pstd) of 1.1701. Comparative analysis against LightGBM, GBRT, CatBoost, SSA-GBRT, and SSA-CatBoost models confirms that SSA-LightGBM delivers superior predictive performance in both accuracy and stability metrics.