摘要
为了更好地预测深部矿山爆破振动速度,针对深部爆破振动速度预测中存在的样本量小、数据分布同浅部爆破不同的问题,将浅部地下矿山爆破数据中有用的知识迁移至深部矿山爆破振动速度预测模型中,提出一种逻辑回归迁移学习算法(LR-TrAdaboost),提升模型的样本容量及预测准确率;以某铜矿深部爆破振动速度预测为研究对象,结合该铜矿27条深部爆破数据以及梅山矿等5个地下金属矿204条浅部爆破数据,利用支持向量回归机(SVR)、回归迁移学习算法(TrAdaboost-R 2)以及LR-TrAdaboost算法分别进行预测和对比。结果表明:3种算法的模型分数分别为0.24、0.38、0.81,均方根误差(RMSE)分别为0.152、0.107、0.06,LR-TrAdaboost算法预测误差相比SVR、TrAdaboost-R^(2)分别降低了60.5%、43.9%;同时,LR-TrAdaboost在迭代次数为50时已经收敛,而TrAdaboost-R^(2)在迭代次数100次后才收敛,收敛速度前者是后者的2倍;LR-TrAdaboost算法的预测性能更好。
In order to better predict the blasting vibration velocity of deep mines,aiming at the problems of small sample size and different data distribution in the prediction of blasting vibration velocity of deep mines,the useful knowledge in the blasting data of shallow underground mines was transferred to the prediction model of blasting vibration velocity of deep mines,and an LR-TrAdaboost(transfer learning)algorithm was proposed to improve the sample size and prediction accuracy of the model.Taking the prediction of deep blasting vibration velocity of a copper mine as the research object,combined with 27 deep blasting data of the copper mine and 204 shallow blasting data of five underground metal mines such as Meishan Mine,SVR,TrAdaboost-R^(2) and LR-TrAdaboost algorithms were used for prediction and comparison respectively.The model scores of the three algorithms are 0.24,0.38 and 0.81,and the root mean square error(RMSE)is 0.152,0.107 and 0.06,respectively.Compared with SVR and TrAdaboost-R^(2),the prediction error of the LR-TrAdaboost algorithm reduces by 60.5%and 43.9%,respectively.At the same time,LR-TrAdaboost converged when the number of iterations is 50,while TrAdaboost-R 2 converged after the number of iterations is 100,and the convergence rate is twice that of the latter.Research shows that the LR-TrAdaboost algorithm has better prediction performance.
作者
张西良
焦灏恺
李二宝
ZHANG Xiliang;JIAO Haokai;LI Erbao(Maanshan Institute of Mining Research Blasting Engineering,Maanshan Anhui 243000,China;State Key Laboratory of Safety and Health for Metal Mines,Maanshan Anhui 243000,China;Sinosteel Maanshan Mine Research Institute Co.,Ltd.,Maanshan Anhui 243000,China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2023年第6期64-72,共9页
China Safety Science Journal
基金
国家重点研发计划项目(2022YFC2904101)。
