LOF与改进SMOTE算法组合的强烈岩爆预测

doi:10.13722/j.cnki.jrme.2020.1035

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Abstract In order to solve low accuracy of strong rock burst prediction resulted from outliers in the rock burst data set and the small number of strong rock bursts. Combination of local outlier factor(LOF) and improved synthetic minority oversampling technique(SMOTE) algorithm is proposed. Firstly，305 groups of rock burst cases collected at home and abroad are used to construct the original rock burst data set，and the averaging method is adopted for non-dimension of the data set. Secondly，the data set structure during data preprocessing stage is improved by using LOF algorithm to eliminate outliers in each rock burst level and through improved SMOTE algorithm to increasing the number of strong rock burst samples at the boundary between strong rock burst samples and medium rock burst samples. Finally，the original rock burst data set and the preprocessed rock burst data set are respectively predicted by six commonly used machine learning models to verify the effectiveness of the preprocessing stage. The results show that the pre-processed rock burst data set has an average increase of 18.35% in the prediction accuracy of the overall rock burst and an average increase of 44.55% in the prediction accuracy of strong rock burst, indicating that combination of LOF and improved SMOTE can effectively improve the accuracy of strong rock burst prediction.

Key words： rock mechanics strong rock burst prediction LOF algorithm outliers improved SMOTE algorithm oversampling machine learning

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	Articles by authors
	TAN Wenkan1
	YE Yicheng1
	2
	HU Nanyan1
	2
	WU Menglong1
	HUANG Zhaoyun3

Cite this article:

TAN Wenkan1,YE Yicheng1,2, et al. Severe rock burst prediction based on the combination of LOF and improved SMOTE algorithm[J]. , 2021, 40(6): 1186-1194.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2020.1035 OR https://rockmech.whrsm.ac.cn/EN/Y2021/V40/I6/1186

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