An Improved Dung Beetle Optimizer with Kernel Extreme Learning Machine for High-Accuracy Prediction of External Corrosion Rates in Buried Pipelines

Accurately predict the external corrosion rate is crucial for the integrity management and risk assessment of buried pipelines. However, existing prediction models often suffer from limitations such as low accuracy, instability, and overfitting. To address these challenges, this study proposes a novel hybrid model, FA-IDBO-KELM. Firstly, Factor Analysis (FA) was employed to reduce the dimensionality of ten original corrosion-influencing factors, extracting seven principal components to mitigate multicollinearity. Subsequently, the hyperparameters (penalty coefficient C and kernel parameter γ) of the Kernel Extreme Learning Machine (KELM) were optimized using an Improved Dung Beetle Optimizer (IDBO). The IDBO included four key enhancements compared to the standard DBO: spatial pyramid mapping (SPM) for population initialization, a spiral search strategy, Lévy flight, and an adaptive t-distribution mutation strategy to prevent premature convergence. The model was validated using a dataset from the West–East Gas Pipeline, with 90% of the data being used for training and 10% for testing. The results demonstrate the superior performance of FA-IDBO-KELM, which achieved a root mean square error (RMSE) of 0.0028, a mean absolute error (MAE) of 0.0021, and a coefficient of determination (R2) of 0.9954 on the test set. Compared to benchmark models (FA-KELM, FA-SSA-KELM, FA-DBO-KELM), the proposed model reduced the RMSE by 93.0%, 89.1%, and 85.3%, and improved the R2 by 85.7%, 10.6%, and 7.4%, respectively. The FA-IDBO-KELM model provides a highly accurate and reliable tool for predicting the external corrosion rate, which can significantly support pipeline maintenance decision-making.