混合优化Stacking集成学习算法的帷幕灌浆注灰量预测OA

For a dam curtain-grouting project,accurate prediction of its unit grout injection volume is crucial to engineering cost control and effective mitigation of its seepage-related risks.To address the complicated nonlinear relationship between its evaluation indicators and unit cement consumption,this paper presents a novel prediction model based on stacking ensemble learning,and compares it with the models of Random Forest(RF),XGBoost,and Support Vector Machine(SVM).We optimize the hyper-parameters of these models using a Newton-Raphson-based optimizer(NRBO),and test their predictive performance based on both training sets and validation sets.Results show the stacking model markedly outperforms the single-method models,with R2=0.998 and RMSE=0.350 achieved on the training sets,and R2=0.971 and RMSE=1.224 on the validation sets.By contrast,validation-set R2 values of the three single-method models are 0.905,0.930 and 0.728 respectively,and RMSEs are 2.219,1.896 and 3.748 respectively.Ensemble-learning models(Stacking,RF,and XGBoost)feature a stronger fitting capacity and robustness in the case of high-dimensional nonlinear data,while the stacking model,by leveraging the strengths of multiple base learners,further enhances predictive accuracy and robustness to outliers.Thus,our NRBO-Stacking model offers high accuracy,effective solutions,and better generalization performance for dam curtain-grouting projects.