# Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines
## 1 Introduction
However, a single machine learning algorithm has its limitations. During training, a single model often suffers from problems such as getting trapped in local minima and having poor generalization performance. Additionally, deep learning network architectures are complex. Much historical data are often required as empirical knowledge during the model training process. The improper selection of hidden layers and nodes in the model architecture will significantly affect the network performance, making over-fitting likely to occur. In recent years, eXtreme Gradient Boosting (XGBoost) has achieved good application results in industry and academia. Many researchers have applied this method to the oil and gas pipeline field. As a cutting-edge algorithm in artificial intelligence, XGBoost has advantages such as fast convergence, strong fitting ability, and exemplary performance in capturing nonlinear and complex relationships. Considering that XGBoost has numerous hyperparameters, optimizing the selection of the XGBoost hyperparameter set can further improve its prediction performance. Bayesian optimization (BO), as a highly effective global optimization algorithm, requires fewer iterations and has a faster convergence speed compared to other hyperparameter optimization methods. This makes it more applicable when computational resources are limited [24], and it has been widely used in various prediction algorithms. Therefore, BO is adopted to optimize the hyperparameters of XGBoost, and a prediction model for the remaining strength of defective pipelines is constructed through BO-XGBoost. However, most current machine learning methods in engineering applications exhibit a \"black box\" form during calculation. The mapping process between the input and output in the learning model is unexplainable. Even if machine learning makes highly accurate predictions, it is difficult to apply machine learning to some high-risk application scenarios due to the poor interpretability of the prediction results. Therefore, it is necessary to strengthen the research on the interpretability of artificial intelligence technology and the judgement and prevention of potential risks. In high-risk fields, relevant studies on the interpretability of machine learning have been carried out, such as medical diagnosis [25], aerospace [26], construction [27], turbulent premixed combustion [28], and other fields [29; 30; 31]. For instance, in the domain of medical diagnosis, the implementation of SHAP analysis has rendered the model's decision-making process both transparent and comprehensible. This ensures that medical experts can understand and trust the model's predictive outcomes, enhancing its practical utility in clinical settings and providing invaluable assistance to physicians in making accurate lung cancer diagnoses. SHAP analysis has been employed in the aerospace sector to elucidate the factors contributing to flight delays, enabling airlines to discern the underlying causes. This approach offers valuable insights into the significance of individual features and their interactions in predicting flight delays, thereby improving the transparency of the model's outputs. The relevant research statistics are shown in Table 1. Compared to LIME or feature permutation, SHAP demonstrates superior performance in the theoretical foundation, global and local feature interpretation, feature interaction, computational efficiency, and visualization. It offers more precise and comprehensive model explanations, making it suitable for interpretability research in complex models. However, to improve the interpretability of the model, the Shapley additive explanation (SHAP) method is adopted to conduct research on the interpretability of the model, analyze the importance of input features to the prediction results, meet the industry's requirements for the interpretability of prediction models, and enhance the guiding effect of the model on production practice.