基于MICE_RF的组合赋权—极限随机树岩爆预测模型

doi:10.11872/j.issn.1005-2518.2022.03.145

Abstract

Abstract:

As a kind of dynamic instability geological disaster with strong abruptness and randomness，rockburst poses a great threat to the safety of personnel，equipment and buildings.Timely and accurate prediction of rockburst grade has become a hot issue in the field of underground engineering.At present，the amount of real training data of rockburst prediction is small and the data is missing.In order to predict the rockburst grade more accurately，a combined weighting-extremely randomized trees（ET） prediction model based on chain random forest multiple interpolation（MICE_RF） algorithm was proposed.According to the characteristics and causes of rockburst，six evaluation indexes including maximum shear stress，uniaxial compressive strength，uniaxial tensile strength，stress coefficient，brittleness coefficient and elastic energy index were selected to form the rockburst evaluation index，and MICE_RF algorithm was used to interpolate the missing data of rockburst data set.Then，a new combined weighting method was proposed，which is the improved analytic hierarchy process（IAHP）-weight determination method based on index correlation（CRITIC），and the weight of each index was comprehensively calculated by using the concept of weight vector distance. Finally，the ET algorithm was used to construct the rockburst prediction model after interpolation，weighting and normalization.Using the existing engineering example data at home and abroad，20 random sampling tests were carried out，and compared with other models to verify the superiority of this model in rockburst grade prediction.In this study，the interpolation effect based on MICE_RF missing value，the combined weighting effect of IAHP-CRITIC index and the comparison of the prediction effects of different models were analyzed and verified respectively.So，the ET rockburst prediction model based on MICE_RF and improved combined weighting was applied and the result of accuracy，precision，recall and RMSE were 93.10%，94.17%，93.44% and 0.2626.The results show that the MICE_RF missing data interpolation method not only increases the available rockburst data set，but also can effectively improve the prediction accuracy of three levels of no rockburst，intermediate rockburst and strong rockburst，and the average prediction accuracy of the complete data set has also been significantly improved.The improved AHP-CRITIC method has more advantages than the previous one，and the ET algorithm is significantly better than other comparison models in the results of four comparison indexes，that is，IAHP-CRITIC-ET model based on MICE_RF can significantly improve the prediction accuracy of rockburst grade，and the prediction results are more stable，which can provide effective guidance for similar projects.

Key words: rockburst grade prediction, missing data, multiple interpolation algorithm of chain random forest （MICE_RF）, combination weighting, weight vector distance, extremely randomized trees（ET） algorithm

CLC Number:

X936

Tingxin WEN,Huanbo SU. Combined Weighting-Extremely Randomized Trees Rockburst Prediction Model Based on MICE_ RF[J].Gold Science and Technology, 2022, 30(3): 392-403.

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评判指标	个案数	缺失个数	最小值	最大值
X₁/MPa	161	27	2.600	167.200
X₂/MPa	161	27	18.320	306.580
X₃	137	51	0.380	22.600
X₄	188	0	0.052	5.263
X₅	164	24	4.479	80.000
X₆	188	0	0.850	10.570
有效个案数	137	-	-	-

评估指标		Mean	SVM	KNN	RF	MCMC	MICE_RF
PAC	r	0.8161	0.8198	0.8295	0.8666	0.8797	0.9114
PAC	RMSE	15.7441	16.1023	16.7919	13.5220	12.7789	10.9850
DAC	K-S statistic	0.1095	0.0985	0.0584	0.0584	0.0364	0.0329
DAC	K-S p-value	0.4520	0.5284	0.8618	0.9100	0.9986	0.9999

序号	岩爆指标						实际类别
序号	X₁	X₂	X₃	X₄	X₅	X₆	实际类别
1	4.60	20.00	3.00	0.230	6.667	1.39	1
2	7.50	52.00	3.70	0.144	14.054	1.30	1
?	?	?	?	?	?	?	?
291	43.40	136.50	7.20	0.318	18.958	5.60	3
292	91.30	225.60	17.20	0.405	13.116	7.30	3

判断矩阵	C₁	C₂	C₃	C₄
X₁权重	0.3062	0.2715	0.2949	0.3604
X₂权重	0.0378	0.0385	0.0412	0.0440
X₃权重	0.0279	0.0285	0.0385	0.0342
X₄权重	0.4284	0.4779	0.3976	0.3685
X₅权重	0.0666	0.0563	0.0624	0.0626
X₆权重	0.1330	0.1273	0.1654	0.1303
X₁权重区间	［0.2715，0.2949，0.3062，0.3604］
X₂权重区间	［0.0378，0.0385，0.0412，0.0440］
X₃权重区间	［0.0279，0.0285，0.0342，0.0385］
X₄权重区间	［0.3685，0.3976，0.4284，0.4779］
X₅权重区间	［0.0563，0.0624，0.0626，0.0666］
X₆权重区间	［0.1273，0.1303，0.1330，0.1654］
最优指标	［0.3083，0.0404，0.0323，0.4181，0.0620，0.1390］

权重向量	X₁	X₂	X₃	X₄	X₅	X₆
w¹	0.3082	0.0404	0.0323	0.4181	0.0620	0.1390
w²	0.1097	0.1046	0.1514	0.3530	0.1769	0.1044
w⁰	0.1891	0.0789	0.1037	0.3790	0.1311	0.1182

Combined Weighting-Extremely Randomized Trees Rockburst Prediction Model Based on MICE_ RF

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数据集	各岩爆等级预测准确率				平均准确率	均方误差
数据集	1	2	3	4	平均准确率	均方误差
插补前	0.7500	0.8000	0.8667	0.6667	0.7955	0.4523
插补后	1.0000	0.8000	0.9375	1.0000	0.9310	0.2626

模型	平均预测准确率	均方误差
ET	0.8966	0.3216
AHP-CRITIC-ET	0.9138	0.2936
IAHP-CRITIC-ET	0.9310	0.2626

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