CLC number:
On-line Access: 2023-01-11
Received: 2022-06-04
Revision Accepted: 2022-08-31
Crosschecked: 2023-01-13
Cited: 0
Clicked: 1742
Citations: Bibtex RefMan EndNote GB/T7714
Fei LV, Jia YU, Jun ZHANG, Peng YU, Da-wei TONG, Bin-ping WU. A novel stacking-based ensemble learning model for drilling efficiency prediction in earth-rock excavation[J]. Journal of Zhejiang University Science A, 2022, 23(12): 1027-1046.
@article{title="A novel stacking-based ensemble learning model for drilling efficiency prediction in earth-rock excavation",
author="Fei LV, Jia YU, Jun ZHANG, Peng YU, Da-wei TONG, Bin-ping WU",
journal="Journal of Zhejiang University Science A",
volume="23",
number="12",
pages="1027-1046",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2200297"
}
%0 Journal Article
%T A novel stacking-based ensemble learning model for drilling efficiency prediction in earth-rock excavation
%A Fei LV
%A Jia YU
%A Jun ZHANG
%A Peng YU
%A Da-wei TONG
%A Bin-ping WU
%J Journal of Zhejiang University SCIENCE A
%V 23
%N 12
%P 1027-1046
%@ 1673-565X
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2200297
TY - JOUR
T1 - A novel stacking-based ensemble learning model for drilling efficiency prediction in earth-rock excavation
A1 - Fei LV
A1 - Jia YU
A1 - Jun ZHANG
A1 - Peng YU
A1 - Da-wei TONG
A1 - Bin-ping WU
J0 - Journal of Zhejiang University Science A
VL - 23
IS - 12
SP - 1027
EP - 1046
%@ 1673-565X
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2200297
Abstract: 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, eXtreme 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.
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