Classification of Coal Bursting Liability Based on Support Vector Machine and Imbalanced Sample Set

Abstract

As an inherent property of the accumulation of elastic energy and the sudden instability failure of coal, coal bursting liability (CBL) is the basis of the research on the early warning and prevention of coal burst. To accurately classify the CBL level, the support-vector-machine (SVM) method was introduced in this paper, and the dynamic failure time (DT), elastic energy index (WET), impact energy index (KE) and uniaxial compressive strength (RC) were selected as the classification indexes. An imbalanced sample set, containing 95 groups of measured data of CBL, was established, and eight SVM classification models were constructed, based on different kernel functions and swarm-intelligence-optimization algorithms. Focusing on the problem of sample imbalance, the classification accuracy, A, F1-score and kappa coefficient were used to comprehensively evaluate the classification performance of SVM models, and the grey-wolf-optimizer SVM (GWO-SVM) model was selected as the best model in this paper, reaching the highest accuracy of 98.9%. The GWO-SVM was applied to identify the CBL level of the 4# coal seam in Xiaozhuang Coal Mine and the 1# coal seam in the Wanfeng Coal Mine. The results of the engineering application are consistent with those from the engineering field, and show that the proposed model is scientific and practical, and can be a new method for CBL classification.