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

Authors

WANG Hao, 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, ChinaFollow
SUN Junqing, 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
ZENG Yifan, National Coal Mine Water Hazard Prevention Engineering Technology Research Center, China University of Mining and Technology (Beijing), Beijing 100083, China
SHANG Hongbo, 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
WANG Tiantian, 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
QIAO Wei, 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

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 (A_c), precision (P_r), recall (R_c), 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 A_c, P_r, R_c, 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.

Keywords

Inner Mongolia-Shaanxi border region, water inrushing from roof bed, inorganic-organic indicator, machine learning, intelligent discrimination

DOI

10.12363/issn.1001-1986.24.01.0083

Recommended Citation

WANG Hao, SUN Junqing, ZENG Yifan, et al. (2024) "An intelligent water source discrimination method for water inrushes from coal seam roofs in the Inner Mongolia-Shaanxi border region," Coal Geology & Exploration: Vol. 52: Iss. 4, Article 9.
DOI: 10.12363/issn.1001-1986.24.01.0083
Available at: https://cge.researchcommons.org/journal/vol52/iss4/9

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