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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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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.

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Acknowledgments

This research was partially supported bythe National Natural Science Foundation Project (Grant Nos. 11472311 and 41272304) of China, the Graduated Students’ Research, Innovation Fund Project (Grant No. CX2011B119) of Hunan Province of China, Project (Grant No. 1343-76140000022) supported by the Scholarship Award for Excellent Doctoral Student of Ministry of Education of China and the Valuable Equipment Open Sharing Fund of Central South University. The authors would like to express thanks to these foundations. The first author would like to thank the Chinese Scholarship Council for financial support to the joint PhD at McGill University, Canada. We also would like to thank the three anonymous referees and editors for their valuable comments and suggestions which improved a previous version of this manuscript.

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Authors and Affiliations

  1. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Jian Zhou & Xibing Li

  2. Department of Mining and Materials Engineering, McGill University, Montreal, QC, H3A 2A7, Canada

    Jian Zhou & Hani S. Mitri

  3. School of Civil Engineering, Henan Polytechnic University, Jiaozuo, 454150, China

    Hani S. Mitri

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Zhou, J., Li, X. & Mitri, H.S. Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction. Nat Hazards 79, 291–316 (2015). https://doi.org/10.1007/s11069-015-1842-3

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