Hydrochemical Prediction of Mine Water Inrush at the Xinli Mine, China

Li, Guoqing; Meng, Zhaoping; Wang, Xinqing; Jian, Yang

doi:10.1007/s10230-016-0405-5

Cited by 16 publications

(5 citation statements)

References 20 publications

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“…In coal mining activities, mine water inrush disasters are an important factor restricting mine safety production [1][2][3][4]. Quickly and accurately identifying the causes of water damage and water source sources can effectively predict and determine the aquifer where water damage is expected to occur or the location of water damage accidents and can also provide guidance for formulating reasonable and effective treatment measures [5,6]. Therefore, the study of efficient and accurate water source identification methods and water source mixing degree calculation models has important guiding significance for safe mining.…”

Section: Introductionmentioning

confidence: 99%

Novel Method on Mixing Degree Quantification of Mine Water Sources: A Case Study

Li,

Fan,

Zhang

et al. 2024

Processes

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After a mine water inrush occurs, it is crucial to quickly identify the source of the water inrush and the key control area, and to formulate accurately efficient water control measures. According to the differences in water chemical characteristics of four aquifers in the Fenyuan coal mine, the concentrations of K+~Na+, Ca2+, Mg2+, Cl−, SO42−, and HCO3− were taken as water source identification indexes. A decision tree classification model based on the C4.5 algorithm was adopted to visualize the chemical characteristics of a single water source and extract rules, and intuitively obtained the discrimination conditions of a single water source with Mg2+, Ca2+, and Cl− as important variables in the decision tree: Mg2+ < 39.585 mg/L, Cl− < 516.338 mg/L and Mg2+ ≥ 39.585 mg/L, Ca2+ < 160.860 mg/L. Factor analysis and Fisher discriminant theory were used to eliminate the redundant ion variables, and the discriminant function equations of the two, three, and four types of mixed water sources were obtained successively in turn. This paper puts forward MSE, RMSE, and MAE as the evaluation indexes of the water source mixing degree calculation models and obtains the ranking of the pros and cons of the mixed water source mixing degree calculation models. The results show that the minimum inscribed circle analytical method is the optimal model for the calculation of the mixing degree of two types of water sources, and the MSE, RMSE, and MAE are 0.17%, 4.13%, and 4.13%, respectively. The minimum inscribed circle clustering method is the optimal model for the calculation of the mixing degree of three types of water sources, and the minimum distance method is the optimal model for the calculation of the mixing degree of four types of water sources. The method of mine water source identification based on the decision tree C4.5 algorithm and mixing degree calculation has the characteristics of a simple calculation process, high efficiency, objective accuracy, and low cost, which can provide a scientific basis for the development of stope water control measures.

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Section: Introductionmentioning

confidence: 99%

Novel Method on Mixing Degree Quantification of Mine Water Sources: A Case Study

Li,

Fan,

Zhang

et al. 2024

Processes

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“…At present, the methods used to determine the mine water inrush source mainly include water temperature and water level measurements (Sui et al, 2010; Lin et al, 2014), hydrochemical analysis (Wang et al, 2020; Li et al, 2016), and mathematical analysis (Wang et al, 2020; Zhang et al, 2019). Under the influence of stratification conditions and circulating environment, the temperature of the groundwater often presents heterogeneity and variability.…”

Section: Introductionmentioning

confidence: 99%

Source identification of mine water inrush based on the exponential whitenization function and the grey situation decision model

Wang¹,

Wang³

et al. 2022

Energy Exploration & Exploitation

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Coal mining at deep levels can cause mine water inrush and groundwater contamination, making it important to accurately and rapidly identify the water inrush source. In this study, 52 water samples were extracted from three types of aquifers in the Linhuan mining area, China. The water sample components Na+ + K+, Ca2+, Mg2+, HCO3−, Cl−, and SO42−, measured in the experiment, were used as evaluation variables, and the piecewise function equation was established by using the exponential whitening function. Finally, combined with water sample data and the CRITIC weighted grey situation decision-making method, the comprehensive membership degree was obtained, and the water inrush source was identified according to the principle of the maximum membership degree. The comprehensive accuracy of the model was 92.3%. The traditional grey situation decision-making method uses the linear whitening function to determine the membership value, ignoring that the value of the whitening function outside the adjacent level is 0, which improves the weight of the adjacent level, causes the loss of effective information, and reduces the discrimination rate. The exponential whitenization function in this paper will solve this problem and further improves the grey situation decision-making method to discriminate the water inrush source, which would also be beneficial regarding the prevention and control of mine water inrush and groundwater contamination.

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“…In recent years, many new ideas and methods have been integrated into mine water gushing prediction with the widespread popularization of machine learning. The main discriminant analysis methods for water source discrimination include: hydration method (Bottrell et al 2008; Li et al 2017; Gu et al 2018; Qian et al 2018), neural network method (Keskin et al 2015; Yang et al 2018; Zhou et al 2018), discriminant model method based on statistical principles (Qiu et al 2017; Chang et al 2018; Liu et al 2018b), clustering analysis (Liu et al 2018a), fuzzy comprehensive evaluation (Yi et al 2012), and gray system model (Zhang and Yang 2018). These methods have been widely used for discriminating water source types and achieved significant results.…”

Section: Introductionmentioning

confidence: 99%

State of the Practice Worldwide: HCA‐PCA‐EWM Discrimination Model of Water Inrush Source in Mines

Huang

Gao

et al. 2022

Groundwater Monitoring Rem

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This column reviews the general features of PHT3D Version 2, a reactive multicomponent transport model that couples the geochemical modeling software PHREEQC-2 (Parkhurst and Appelo 1999) with three-dimensional groundwater flow and transport simulators MODFLOW-2000 and MT3DMS (Zheng and Wang 1999). The original version of PHT3D was developed by Henning Prommer and Version 2 by Henning Prommer and Vincent Post (Prommer and Post 2010). More detailed information about PHT3D is available at the website http://www.pht3d.org.The review was conducted separately by two reviewers. This column is presented in two parts.

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Hydrochemical Prediction of Mine Water Inrush at the Xinli Mine, China

Cited by 16 publications

References 20 publications

Novel Method on Mixing Degree Quantification of Mine Water Sources: A Case Study

Novel Method on Mixing Degree Quantification of Mine Water Sources: A Case Study

Source identification of mine water inrush based on the exponential whitenization function and the grey situation decision model

State of the Practice Worldwide: HCA‐PCA‐EWM Discrimination Model of Water Inrush Source in Mines

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