蒙陕接壤区煤层顶板涌水水源智能判别方法  

An intelligent water source discrimination method for water inrushes from coal seam roofs in the Inner Mongolia-Shaanxi border region

作　　者：王皓[1,2,3] 孙钧青 曾一凡 尚宏波 王甜甜 乔伟 WANG Hao;SUN Junqing;ZENG Yifan;SHANG Hongbo;WANG Tiantian;QIAO Wei(China Coal Research Institute,Beijing 100013,China;CCTEG Xi’an Research Institute(Group)Co.,Ltd.,Xi’an 710077,China;Shaanxi Key Laboratory of Prevention and Control Technology for Coal Mine Water Hazard,Xi’an 710077,China;National Coal Mine Water Hazard Prevention Engineering Technology Research Center,China University of Mining and Technology(Beijing),Beijing 100083,China)

机构地区：[1]煤炭科学研究总院,北京100013 [2]中煤科工西安研究院(集团)有限公司,陕西西安710077 [3]陕西省煤矿水害防治技术重点实验室,陕西西安710077 [4]中国矿业大学(北京)国家煤矿水害防治工程技术研究中心,北京100083

出　　处：《煤田地质与勘探》2024年第4期76-88,共13页Coal Geology & Exploration

基　　金：国家自然科学基金项目(52204262);陕西省自然科学基础研究计划项目(2022JQ-471);中国煤炭科工集团有限公司科技创新创业资金专项重点项目(2023-TD-ZD001-001)。

摘　　要：蒙陕接壤区煤炭高强度开采诱发的煤层顶板水害问题日益凸显,高效智能地判别煤层顶板涌水水源是顶板水害防治的关键。以蒙陕接壤区3个典型矿井为研究对象,将无机指标K^(+)+Na^(+)、Ca^(2+)、Mg^(2+)、Cl^(-)、SO_(4)^(2-)、HCO_(3)^(-)、TDS和有机指标UV_(254)、TOC、溶解性有机质(DOM)的荧光光谱作为判别指标,利用主成分分析法(PCA)对80组地下水水样数据进行主成分提取,提出一种人工鱼群算法(AFSA)改进随机森林(RF)的PCA-AFSA-RF顶板涌水水源智能判别方法。首先,建立PCA-RF判别模型,其准确率(A_(c))、精确率(P_(r))、召回率(R_(c))和F-measure指数(f_(1))分别达到了83.00%、83.17%、80.42%和79.57%;其次,通过AFSA对PCA-RF判别模型中决策树数目、树深和内部节点分裂所需的最小样本数进行寻优,在AFSA中引入遗传机制以避免陷入局部最优,建立基于PCA-AFSA-RF的煤层顶板涌水水源智能判别模型,该模型A_(c)、P_(r)、R_(c)、f_(1)分别达到92.18%、91.11%、87.58%和88.82%,较PCA-RF分别提高9.18%、7.94%、7.16%和9.25%,回代准确率达到97.50%;最后,利用该模型对12个矿井水水样进行判别,结果与现场实际相一致,表明AFSA改进后的PCA-RF模型具有更好的准确性和泛化能力。研究结果可为煤层顶板涌水水源的准确判别提供新方法。Water hazard on the coal seam proof induced by high-intensity coal mining are increasingly prominent in the Inner Mongolia-Shaanxi border region.The effective,accurate water-source discrimination of the water inrushes is the key to water hazard prevention.This study investigated three typical mines in the Inner Mongolia-Shaanxi border region.To this end,principal component analysis(PCA)was employed to extract principal components from 80 groups of groundwater samples.Then,with inorganic indicators K^(+)+Na^(+),Ca^(2+),Mg^(2+),Cl^(-),SO_(4)^(2-),HCO_(3)^(-)and TDS and organic indicators UV254,TOC,and dissolved organic matter(DOM)’s fluorescence spectra as discriminant indicators,this study proposed a intelligent identificaton method of PCA-AFSA-RF roof water inrush source by using artificial fish swarm algorithm(AFSA)to improve random forest(RF).First,a PCA-RF discriminant model was established,with accuracy(A_(c)),precision(P_(r)),recall(R_(c)),and F-measure(f_(1))of 83.00%,83.17%,80.42%,and 79.57%,respectively.Then,in the PCA-RF discriminant model,AFSA was employed to optimize the number of decision trees,the depth of trees,and the minimum sample number needed for internal node splitting.Furthermore,a genetic mechanism was introduced into AFSA to avoid local optimization.In this way,a PCA-AFSA-RF water-source discriminant model for water inrushes on coal seam roofs was established,with A_(c),P_(r),R_(c),and f_(1) of up to 92.18%,91.11%,87.58%,and 88.82%,respectively,increasing by 9.18%,7.94%,7.16%,and 9.25% compared to the PCA-RF model.Furthermore,the PCA-AFSA-RF exhibited a back substitution accuracy reaching 97.50%.Finally,this model was used for the water-source discrimination of 12 water samples from the mines,yielding results consistent with the actual results in the field.This indicates that the PCA-RF model with improved AFSA enjoys better accuracy and generalization ability.The research results of this study can provide a new method for the accurate water-source identification of water inrushes from co

关 键 词：蒙陕接壤区 顶板涌水 无机-有机指标 机器学习 智能判别 

分 类 号：TD745[矿业工程—矿井通风与安全]