基于KPCA-MPSO-ELM的矿井突水水源判别模型  

KPCA-MPSO-ELM based model for discrimination of mine water inrush source

作　　者：毛志勇[1] 黄春娟 路世昌[1] 韩榕月 MAO Zhiyong;HUANG Chunjuan;LU Shichang;HAN Rongyue(School of Business Administration,Liaoning Technical University,Huludao Liaoning 125105,China)

机构地区：[1]辽宁工程技术大学工商管理学院

出　　处：《中国安全科学学报》2018年第8期111-116,共6页China Safety Science Journal

基　　金：国家自然科学基金资助(70971059)

摘　　要：为准确判别矿井突水水源并有效预防突水事故,提出一种基于核主成分分析-改进粒子群算法-极限学习机（KPCA-MPSO-ELM）的矿井突水水源判别模型。利用核主成分分析（KPCA）法对原始数据进行属性约减,通过改进粒子群算法（MPSO）优化极限学习机（ELM）的初始权值和阈值,建立KPCA-MPSO-ELM模型;在综合考虑矿井各含水层的水化学特征的基础上,选取Ca2＋、Mg2＋、K＋＋Na＋、HCO3-、SO42-、Cl-等的浓度和总硬度作为矿井突水水源的主要判别依据;以新庄孜矿的45组实测数据作为样本进行实例分析,其中33组数据作为训练数据训练模型,另外12组数据作为预测样本,用该模型进行预测,并将其判别结果与MPSO-ELM、KPCA-PSO-ELM模型的判别结果进行对比。结果表明：KPCA方法能减少指标数据间的信息重叠;通过MPSO优化ELM参数,可提高算法的整体搜索性能和收敛速度; KPCA-MPSO-ELM模型的预测精度高于MPSO-ELM、KPCA-PSOELM等2个模型。In order to discriminate the source of water inrush accurately and prevent water inrush accidents,a KPCA-MPSO-ELM based model was built for discriminating mine water inrush source. For building the model,KPCA was used to reduce the attributes of data and to optimize the initial weights and thresholds of ELM by MPSO. Hydrochemical characteristics of each aquifer in the mine were considered,seven indicators of Ca2＋,Mg2＋,K＋＋Na＋,HCO-3,SO2-4,Cl-and total hardness were selected as a basis for judging water inrush. The 45 groups of measured data from Xinzhuangzi mine was selected as an example for analysis.Thirty-three groups of data were used as training data to train the model and other 12 groups of data were used as prediction samples. A comparison was made between the discrimination results by KPCA-MPSO-ELM based model and those by MPSO-ELM and KPCA-PSO-ELM models. The results show that KPCA analysis reduces information overlap between indicator data,that optimizing ELM parametersthrough MPSO improves the overall search performance and convergence speed of the model,and that the prediction accuracy of KPCA-MPSO-ELM model is higher than that of other two models.

关 键 词：矿井突水 水源判别 核主成分分析(KPCA) 改进粒子群算法(MPSO) 极限学习机(ELM) 

分 类 号：X924.4[环境科学与工程—安全科学]