蒙陕接壤区煤层顶板涌水水源智能判别方法OA北大核心CSTPCD
An intelligent water source discrimination method for water inrushes from coal seam roofs in the Inner Mongolia-Shaanxi border region
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+、Ca2+、Mg2+、Cl-、SO4 2-、HCO、-3TDS和有机指标UV254、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(Ac)、精确率(Pr)、召回率(Rc)和F-measure指数(f1)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Ac、Pr、Rc、f1 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+,Ca2+,Mg2+,Cl-,SO4 2-,HCO3-and TDS and organic in-dicators 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 al-gorithm(AFSA)to improve random forest(RF).First,a PCA-RF discriminant model was established,with accuracy(Ac),precision(Pr),recall(Rc),and F-measure(f1)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 AF-SA 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 Ac,Pr,Rc,and f1 of up to 92.18%,91.11%,87.58%,and 88.82%,respectively,in-creasing by 9.18%,7.94%,7.16%,and 9.25%compared to the PCA-RF model.Furthermore,the PCA-AFSA-RF exhib-ited 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 coal seam roofs.
王皓;孙钧青;曾一凡;尚宏波;王甜甜;乔伟
煤炭科学研究总院,北京 100013||中煤科工西安研究院(集团)有限公司,陕西 西安 710077||陕西省煤矿水害防治技术重点实验室,陕西 西安 710077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中煤科工西安研究院(集团)有限公司,陕西 西安 710077||陕西省煤矿水害防治技术重点实验室,陕西 西安 710077
矿山工程
蒙陕接壤区顶板涌水无机-有机指标机器学习智能判别
Inner Mongolia-Shaanxi border regionwater inrushing from roof bedinorganic-organic indicatormachine learningintelligent discrimination
《煤田地质与勘探》 2024 (004)
76-88 / 13
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)
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