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Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction

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  • Jian Zhou
  • Xibing Li
  • Hani Mitri

Abstract

The prediction of pillar stability (PS) in hard rock mines is a crucial task for which many techniques and methods have been proposed in the literature including machine learning classification. In order to make the best use of the large variety of statistical and machine learning classification methods available, it is necessary to assess their performance before selecting a classifier and suggesting improvement. The objective of this paper is to compare different classification techniques for PS detection in hard rock mines. The data of this study consist of six features, namely pillar width, pillar height, the ratio of pillar width to its height, uniaxial compressive strength of the rock, pillar strength, and pillar stress. A total of 251 pillar cases between 1972 and 2011 are analyzed. Six supervised learning algorithms, including linear discriminant analysis, multinomial logistic regression, multilayer perceptron neural networks, support vector machine (SVM), random forest (RF), and gradient boosting machine, are evaluated for their ability to learn for PS based on different input parameter combinations. In this study, the available data set is randomly split into two parts: training set (70 %) and test set (30 %). A repeated tenfold cross-validation procedure (ten repeats) is applied to determine the optimal parameter values during modeling, and an external testing set is employed to validate the prediction performance of models. Two performance measures, namely classification accuracy rate and Cohen’s kappa, are employed. The analysis of the accuracy together with kappa for the PS data set demonstrates that SVM and RF achieve comparable median classification accuracy rate and Cohen’s kappa values. All models are fitted by “R” programs with the libraries and functions described in this study. Copyright Springer Science+Business Media Dordrecht 2015

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  • Jian Zhou & Xibing Li & Hani Mitri, 2015. "Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(1), pages 291-316, October.
  • Handle: RePEc:spr:nathaz:v:79:y:2015:i:1:p:291-316
    DOI: 10.1007/s11069-015-1842-3
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    References listed on IDEAS

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    1. Zaobao Liu & Jianfu Shao & Weiya Xu & Yongdong Meng, 2013. "Prediction of rock burst classification using the technique of cloud models with attribution weight," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(2), pages 549-568, September.
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    1. Zongguo Zhang & Xianyang Qiu & Xiuzhi Shi & Zhi Yu, 2023. "Chamber roof deformation prediction and analysis of underground mining using experimental design methodologies," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(1), pages 757-777, January.
    2. Danial Jahed Armaghani & Panagiotis G. Asteris & Behnam Askarian & Mahdi Hasanipanah & Reza Tarinejad & Van Van Huynh, 2020. "Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    3. Weizhang Liang & Suizhi Luo & Guoyan Zhao & Hao Wu, 2020. "Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms," Mathematics, MDPI, vol. 8(5), pages 1-17, May.
    4. Ahmed Salih Mohammed & Panagiotis G. Asteris & Mohammadreza Koopialipoor & Dimitrios E. Alexakis & Minas E. Lemonis & Danial Jahed Armaghani, 2021. "Stacking Ensemble Tree Models to Predict Energy Performance in Residential Buildings," Sustainability, MDPI, vol. 13(15), pages 1-22, July.
    5. Muhammad Ali & Naseer Muhammad Khan & Qiangqiang Gao & Kewang Cao & Danial Jahed Armaghani & Saad S. Alarifi & Hafeezur Rehman & Izhar Mithal Jiskani, 2023. "Prediction of Coal Dilatancy Point Using Acoustic Emission Characteristics: Insight Experimental and Artificial Intelligence Approaches," Mathematics, MDPI, vol. 11(6), pages 1-25, March.
    6. Chongchong Qi & Andy Fourie & Xuhao Du & Xiaolin Tang, 2018. "Prediction of open stope hangingwall stability using random forests," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(2), pages 1179-1197, June.
    7. Ning Li & Masoud Zare & Congke Yi & Rafael Jimenez, 2022. "Stability Risk Assessment of Underground Rock Pillars Using Logistic Model Trees," IJERPH, MDPI, vol. 19(4), pages 1-19, February.
    8. Chao Chen & Jian Zhou & Tao Zhou & Weixun Yong, 2021. "Evaluation of vertical shaft stability in underground mines: comparison of three weight methods with uncertainty theory," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(2), pages 1457-1479, November.

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