Abstract
Five hybrid artificial intelligence (AI) approaches based on machine learning (ML) and metaheuristic algorithms were proposed to predict open stope hangingwall (HW) stability. The ML algorithms consisted of logistic regression (LR), multilayer perceptron neural networks (MLPNN), decision tree (DT), gradient boosting machine (GBM), and support vector machine (SVM), and the firefly algorithm (FA) was used to tune their hyperparameters. The objectives are to compare different hybrid AI approaches for HW stability prediction and investigate the relative importance of its influencing variables. Performance measures were chosen to be the confusion matrix, the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC). The results showed that the proposed hybrid AI approaches had great potential to predict HW stability and the FA was efficient in ML hyperparameters tuning. The AUC values of the optimum GBM, SVM, and LR models on the testing set were 0.855, 0.816, and 0.801, respectively, denoting that their performance was excellent. The optimum GBM model with the top left cutoff or the Youden's cutoff was recommended for HW prediction in terms of the accuracy, the true positive rate and the AUC value. The relative importance of influencing variables on HW stability was obtained, in which stope design method was found to be the most significant variable.
| Original language | English |
|---|---|
| Article number | 04017086 |
| Journal | Journal of Computing in Civil Engineering |
| Volume | 32 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 1 Mar 2018 |
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Comparative Study of Hybrid Artificial Intelligence Approaches for Predicting Hangingwall Stability. / Qi, Chongchong; Fourie, Andy; Ma, Guowei; Tang, Xiaolin; Du, Xuhao.
In: Journal of Computing in Civil Engineering, Vol. 32, No. 2, 04017086, 01.03.2018.Research output: Contribution to journal › Article
TY - JOUR
T1 - Comparative Study of Hybrid Artificial Intelligence Approaches for Predicting Hangingwall Stability
AU - Qi, Chongchong
AU - Fourie, Andy
AU - Ma, Guowei
AU - Tang, Xiaolin
AU - Du, Xuhao
PY - 2018/3/1
Y1 - 2018/3/1
N2 - Five hybrid artificial intelligence (AI) approaches based on machine learning (ML) and metaheuristic algorithms were proposed to predict open stope hangingwall (HW) stability. The ML algorithms consisted of logistic regression (LR), multilayer perceptron neural networks (MLPNN), decision tree (DT), gradient boosting machine (GBM), and support vector machine (SVM), and the firefly algorithm (FA) was used to tune their hyperparameters. The objectives are to compare different hybrid AI approaches for HW stability prediction and investigate the relative importance of its influencing variables. Performance measures were chosen to be the confusion matrix, the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC). The results showed that the proposed hybrid AI approaches had great potential to predict HW stability and the FA was efficient in ML hyperparameters tuning. The AUC values of the optimum GBM, SVM, and LR models on the testing set were 0.855, 0.816, and 0.801, respectively, denoting that their performance was excellent. The optimum GBM model with the top left cutoff or the Youden's cutoff was recommended for HW prediction in terms of the accuracy, the true positive rate and the AUC value. The relative importance of influencing variables on HW stability was obtained, in which stope design method was found to be the most significant variable.
AB - Five hybrid artificial intelligence (AI) approaches based on machine learning (ML) and metaheuristic algorithms were proposed to predict open stope hangingwall (HW) stability. The ML algorithms consisted of logistic regression (LR), multilayer perceptron neural networks (MLPNN), decision tree (DT), gradient boosting machine (GBM), and support vector machine (SVM), and the firefly algorithm (FA) was used to tune their hyperparameters. The objectives are to compare different hybrid AI approaches for HW stability prediction and investigate the relative importance of its influencing variables. Performance measures were chosen to be the confusion matrix, the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC). The results showed that the proposed hybrid AI approaches had great potential to predict HW stability and the FA was efficient in ML hyperparameters tuning. The AUC values of the optimum GBM, SVM, and LR models on the testing set were 0.855, 0.816, and 0.801, respectively, denoting that their performance was excellent. The optimum GBM model with the top left cutoff or the Youden's cutoff was recommended for HW prediction in terms of the accuracy, the true positive rate and the AUC value. The relative importance of influencing variables on HW stability was obtained, in which stope design method was found to be the most significant variable.
KW - Firefly algorithm
KW - Hangingwall stability prediction
KW - Hybrid artificial intelligence (AI) approaches
KW - Machine learning
KW - Performance comparison
KW - Variable importance
UR - http://www.scopus.com/inward/record.url?scp=85038935133&partnerID=8YFLogxK
U2 - 10.1061/(ASCE)CP.1943-5487.0000737
DO - 10.1061/(ASCE)CP.1943-5487.0000737
M3 - Article
VL - 32
JO - Journal of Computing in Civil Engineering
JF - Journal of Computing in Civil Engineering
SN - 0887-3801
IS - 2
M1 - 04017086
ER -