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

摘要/Abstract

摘要：

目前岩爆预测的真实训练数据量小、数据存在缺失，为了更加准确地预测岩爆等级，提出了一种基于链式随机森林多重插补（MICE_RF）算法的组合赋权—极限随机树（ET）预测模型。首先，在选取岩爆灾害主要评判指标的基础上，采用MICE_RF算法插补缺失数据；然后，由改进层次分析法（IAHP）和基于指标相关性的权重确定方法（CRITIC）确定指标主、客观权重，并引入权向量距离概念对指标组合赋权；最后，将插补和赋权后数据集采用ET算法，构建岩爆等级预测模型。利用国内外工程实例数据进行20次随机抽样试验，并与其他模型进行对比分析。结果表明：MICE_RF插补后可显著提高岩爆模型预测效果；改进AHP-CRITIC法较改进前更具优势，该模型平均预测准确率为93.10%，各比较指标结果均优于对比模型，预测结果更稳定。

关键词: 岩爆等级预测, 数据缺失, 链式随机森林的多重插补（MICE_RF）算法, 组合赋权, 权向量距离, 极限随机树（ET）算法

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

中图分类号:

X936

温廷新,苏焕博. 基于MICE_RF的组合赋权—极限随机树岩爆预测模型[J]. 黄金科学技术, 2022, 30(3): 392-403.

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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参考文献 0

	Acurna E， Rodriguez C，2004.The treatment of missing values and its effect in the classifier accuracy［C］//Proceedings of the Meeting of the International Federation of Classification Societies （IFCS）.Chicago：International Federation Classification Societies： 639-647.
	Afraei S， Shahriar K， Madani S H，2019.Developing intelligent classification models for rockburst prediction after recognizing significant predictor variables，Section 1：Literature review and data preprocessing procedure［J］.Tunnelling and Underground Space Technology，83：324-353.
	Boshuizen H C， Knook D L，1999.Multiple imputation of missing blood pressure covariates in survival analysis［J］.Statistics in Medicine，（7）：681-694.
	Chen Juan， Wang Xianyu， Luo Lingling，et al，2020.Missing value filling effect：A comparison between machine learning and statistical learning［J］.Statistics and Decision Making，36 （17）：28-32.
	Diakoulaki D， Mavrotas G， Papayannakis L，1995.Determining objective weights in multiple criteria problems：The CRITIC method［J］.Computers and Operations Research，22（7）：763-770.
	Huang Jian， Xia Yuanyou， Lin Manqing，2019.Study on multi-dimensional cloud model prediction of rockburst based on improved combination weighting［J］.Chinese Journal of Safety Science，29 （7）：26-32.
	Li Mingliang， Li Kegang， Qin Qingci，et al，2021.Discussion and selection of machine learning algorithm model for rockburst intensity grade prediction ［J］.Chinese Journal of Rock Mechanics and Engineering，40 （Supp.1）：2806-2816.
	Li Renhao， Gu Helong， Li Xibing，et al，2020.A PSO-RBF neural network model for rockburst tendency prediction ［J］.Gold Science and Technology，28（1）：134-141.
	Liu Fei，2020.Study on the Evolution and Warning of Rockbursts in Deep-buried Tunnels of the Hanjiang-to-Weihe River Diversion Project by Microseismic Monitoring ［D］.Dalian：Dalian University of Technology.
	Liu Fengqin，2009.Multiple imputation of missing values of income variables based on chain equation［J］.Statistical Research，26 （1）：71-77.
	Long Yanfang，2017.Research on Short-term Traffic Flow Prediction Model Based on Ensembles of Extremely Randomized Trees［D］.Changsha：Hunan University.
	Lu Furan， Chen Jianhong，2018.Rockburst prediction method based on AHP and entropy weight TOPSIS model ［J］.Gold Science and Technology，26（3）：365-371.
	Nugroho H， Utama N P， Surendro K，2021.Class center-based firefly algorithm for handling missing data［J］.Journal of Big Data，8（1）：1-14.
	Qian Chao， Chen Jianxun， Luo Yanbin，et al，2016.Missing data interpolation method for highway tunnel operation based on random forest ［J］.Transportation System Engineering and Information，16 （3）：81-87.
	Saaty T L，1994.How to make a decision：The analytic hierarchy process［J］.Interfaces，24（6）：19-43.
	Shang Huandi， Wang Ping， Pei Mingsong，et al，2017.Rockburst prediction based on rough set and weighted grey correlation analysis［J］.Industrial Safety and Environmental Protection，43 （6）：47-51.
	Song Liang， Wan Jianzhou，2020.Comparative study on missing data interpolation methods［J］.Statistics and Decision Ma-king，36（18）：10-14.
	Tan Wenkan， Ye Yicheng， Hu Nanyan，et al，2021.Strong rockburst prediction based on LOF and improved SMOTE algorithm［J］.Chinese Journal of Rock Mechanics and Engine-ering，40（6）：1186-1194.
	Tang Zhili， Xu Qianjun，2020.Research on rockburst prediction based on nine machine learning algorithms［J］.Chinese Journal of Rock Mechanics and Engineering，39（4）：773-781.
	Tian Rui， Meng Haidong， Chen Shijiang，et al，2020a.Prediction of intensity classification of rockburst based on deep neural network ［J］.Journal of China Coal Society，45（Supp.1）：191-201．
	Tian Rui， Meng Haidong， Chen Shijiang，et al，2020b.Prediction model of rockburst intensity classification based on RF-AHP-Cloud model［J］.Chinese Journal of Safety Science，30（7）：166-172．
	Wang Junxia， Zhang Yu， Yan Zheming，et al，2013.Research on performance evaluation of rural public goods supply based on combination weighting method［J］.Journal of Northwest University（Philosophy and Social Sciences Edition），43（2）：117-121.
	Wang Xianlong， Feng Zao， Zhao Yanfeng，2021.An active learning method for unbalanced sample set of pipeline blockage［J］.Chemical Automation and Instrumentation，48（3）：222-231.
	Wang Yuanhan， Li Wodong， Li Qiguang，et al，1998 .Fuzzy mathematics comprehensive evaluation method for rockburst prediction［J］.Chinese Journal of Rock Mechanics and Engineering，（5）：15-23.
	Wu H W， Zhen J， Zhang J，2020.Urban rail transit operation safety evaluation based on an improved CRITIC method and cloud model［J］.Journal of Rail Transport Planning & Management，16：100206..
	Wu Tongyu， Wu Shaoxiong，2018.Missing value interpolation of statistical data based on kernel principal component analysis and particle swarm optimization support vector machine［J］.Statistics and Decision Making，34（8）：21-24.
	Xie X， Jiang W， Guo J，2021.Research on rockburst prediction classification based on GA-XGB model［J］.IEEE Access，9：83993-84020.
	Xie Xuebin， Li Dexuan， Kong Lingyan，et al，2020.Prediction model of rockburst tendency grade based on CRITIC-XGB algorithm［J］.Chinese Journal of Rock Mechanics and Engineering，39（10）：1975-1982.
	Yin Xin， Liu Quansheng， Wang Xinyu，et al，2020.Prediction model of rockburst intensity classification based on combined weighting and attribute interval recognition theory ［J］. Journal of China Coal Society，45（11）：3772-3780．
	Zhang Xiangyu，2021.Study on Rock Burst Mechanism and Comprehensive Prediction Method of Rock Mass with Structural Plane ［D］.Jinan：Shandong University.
	Zheng Y， Zhong H， Fang Y，et al，2019.Rockburst prediction model based on entropy weight integrated with grey relational BP neural network［J］.Advances in Civil Engineering，（4）：1-8..
	陈娟，王献雨，罗玲玲，等，2020.缺失值填补效果：机器学习与统计学习的比较［J］.统计与决策，36（17）：28-32.
	黄建，夏元友，吝曼卿，2019.基于改进组合赋权的岩爆多维云模型预测研究［J］.中国安全科学学报，29（7）：26-32.
	李明亮，李克钢，秦庆词，等，2021.岩爆烈度等级预测的机器学习算法模型探讨及选择［J］.岩石力学与工程学报，40（增1）：2806-2816.
	李任豪，顾合龙，李夕兵，等，2020.基于PSO-RBF神经网络模型的岩爆倾向性预测［J］.黄金科学技术，28（1）：134-141.
	刘飞，2020.引汉济渭深埋隧洞岩爆孕育特征与微震监测预警研究［D］.大连：大连理工大学.
	刘凤芹，2009.基于链式方程的收入变量缺失值的多重插补［J］.统计研究，26（1）：71-77.
	龙艳芳，2017.基于极限随机树集成的短时交通流预测模型研究［D］.长沙：湖南大学.
	卢富然，陈建宏，2018.基于AHP和熵权TOPSIS模型的岩爆预测方法［J］.黄金科学技术，26（3）：365-371.
	钱超，陈建勋，罗彦斌，等，2016.基于随机森林的公路隧道运营缺失数据插补方法［J］.交通运输系统工程与信息，16（3）：81-87.
	商欢迪，王平，裴明松，等，2017.基于粗糙集和加权灰色关联分析的岩爆预测［J］.工业安全与环保，43（6）：47-51.
	宋亮，万建洲，2020.缺失数据插补方法的比较研究［J］.统计与决策，36（18）：10-14.
	谭文侃，叶义成，胡南燕，等，2021.LOF与改进SMOTE算法组合的强烈岩爆预测［J］.岩石力学与工程学报，40（6）：1186-1194.
	汤志立，徐千军，2020.基于9种机器学习算法的岩爆预测研究［J］.岩石力学与工程学报，39（4）：773-781.
	田睿，孟海东，陈世江，等，2020a.基于深度神经网络的岩爆烈度分级预测［J］.煤炭学报，45（增1）：191-201.
	田睿，孟海东，陈世江，等，2020b.RF-AHP-云模型下岩爆烈度分级预测模型［J］.中国安全科学学报，30（7）：166-172.
	王俊霞，张玉，鄢哲明，等，2013.基于组合赋权方法的农村公共产品供给绩效评价研究［J］.西北大学学报（哲学社会科学版），43（2）：117-121.
	王显龙，冯早，赵燕锋，2021.一种面向管道堵塞不均衡样本集的主动学习方法［J］.化工自动化及仪表，48（3）：222-231.
	王元汉，李卧东，李启光，等，1998.岩爆预测的模糊数学综合评判方法［J］.岩石力学与工程学报，（5）：15-23.
	吴桐雨，吴少雄，2018.基于核主成分分析和粒子群优化支持向量机的统计数据缺失值插补［J］.统计与决策，34（8）：21-24.
	谢学斌，李德玄，孔令燕，等，2020.基于CRITIC-XGB算法的岩爆倾向等级预测模型［J］.岩石力学与工程学报，39（10）：1975-1982.
	殷欣，刘泉声，王心语，等，2020.基于组合赋权和属性区间识别理论的岩爆烈度分级预测模型［J］.煤炭学报，45（11）：3772-3780.
	张翔宇，2021.含结构面岩体岩爆发生机理及综合预测方法研究［D］.济南：山东大学.

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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