王皓,孙钧青,曾一凡,等. 蒙陕接壤区煤层顶板涌水水源智能判别方法[J]. 煤田地质与勘探,2024,52(4):76−88. DOI: 10.12363/issn.1001-1986.24.01.0083
引用本文: 王皓,孙钧青,曾一凡,等. 蒙陕接壤区煤层顶板涌水水源智能判别方法[J]. 煤田地质与勘探,2024,52(4):76−88. DOI: 10.12363/issn.1001-1986.24.01.0083
WANG Hao,SUN Junqing,ZENG Yifan,et al. An intelligent water source discrimination method for water inrushes from coal seam roofs in the Inner Mongolia-Shaanxi border region[J]. Coal Geology & Exploration,2024,52(4):76−88. DOI: 10.12363/issn.1001-1986.24.01.0083
Citation: WANG Hao,SUN Junqing,ZENG Yifan,et al. An intelligent water source discrimination method for water inrushes from coal seam roofs in the Inner Mongolia-Shaanxi border region[J]. Coal Geology & Exploration,2024,52(4):76−88. DOI: 10.12363/issn.1001-1986.24.01.0083

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

  • 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+、Ca2+、Mg2+、Cl、SO4 2−、HCO3 、TDS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AcPrRcf1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。

     

    Abstract: 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 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 (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 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 Ac, Pr, Rc, and f1 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 coal seam roofs.

     

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