A novel stacking-based ensemble learning model for drilling efficiency prediction in earth-rock excavation

作     者：Fei LV Jia YU Jun ZHANG Peng YU Da-wei TONG Bin-ping WU Fei LV;Jia YU;Jun ZHANG;Peng YU;Da-wei TONG;Bin-ping WU

作者机构：State Key Laboratory of Hydraulic Engineering Simulation and SafetyTianjin UniversityTianjin 300350China 

出 版 物：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 (浙江大学学报（英文版）A辑（应用物理与工程）)

年 卷 期：2022年第23卷第12期

页      面：1027-1046页

核心收录：

学科分类：0820[工学-石油与天然气工程] 08[工学] 081104[工学-模式识别与智能系统] 081401[工学-岩土工程] 0814[工学-土木工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Yalong River Joint Funds of the National Natural Science Foundation of China(No.U1965207) the National Natural Science Foundation of China(Nos.51839007,51779169,and 52009090)。 

主　　题：Drilling efficiency Prediction Earth-rock excavation Stacking-based ensemble learning Improved cuckoo search optimization(ICSO)algorithm Comprehensive effects of various factors Hyper-parameter optimization 

摘      要：Accurate prediction of drilling efficiency is critical for developing the earth-rock excavation schedule.The single machine learning(ML)prediction models usually suffer from problems including parameter sensitivity and overfitting.In addition,the influence of environmental and operational factors is often ignored.In response,a novel stacking-based ensemble learning method taking into account the combined effects of those factors is proposed.Through multiple comparison tests,four models,e Xtreme gradient boosting(XGBoost),random forest(RF),back propagation neural network(BPNN)as the base learners,and support vector regression(SVR)as the meta-learner,are selected for stacking.Furthermore,an improved cuckoo search optimization(ICSO)algorithm is developed for hyper-parameter optimization of the ensemble model.The application to a real-world project demonstrates that the proposed method outperforms the popular single ML method XGBoost and the ensemble model optimized by particle swarm optimization(PSO),with 16.43%and 4.88%improvements of mean absolute percentage error(MAPE),respectively.