Study on Shaft Soft Rock Deformation Prediction Based on Weighted Improved Stacking Ensemble Learning

In recent years, deformation disasters in mine shafts have occurred frequently, posing a threat to mine safety. The nonlinear coupling relationship between shaft surrounding rock deformation and rock mass mechanical parameters is a key criterion for surrounding rock stability. However, existing machine learning prediction methods are rarely applied to shaft deformation, and issues such as poor accuracy and generalization of single models remain. To address this, the study proposes a feature-weighted Stacking ensemble model, which considers 15 feature variables; using RMSE, MAE, R2, and inter-model MAPE correlation as evaluation metrics, GBDT, XGBoost, KNN, and MLP are selected as base learners, with Lasso linear regression as the meta-learner. Prediction errors are corrected by weighting the outputs of base learners based on prediction accuracy. Experiments show that, using MAPE as the evaluation metric, the improved model reduces the error by 2.59% compared with the best base learner KNN, by 6.83% compared with XGBoost, and by 0.18% more than the traditional Stacking algorithm, making it suitable for predicting weak surrounding rock shaft deformation under multi-feature conditions.