Identification model of water inrush source based on statistical analysis in Fengyu minefield, Northwest China

Song, Chenguang; Yao, Lei; Gao, Jun; Hua, Chengya; Ni, Qihang

doi:10.1007/s12517-021-06901-1

Cited by 9 publications

(6 citation statements)

References 42 publications

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“…Among multivariate statistical methods, cluster analysis [16,17] and discriminant analysis are commonly used [18,19]. The introduction of hybrid calculations of multiple linear analyses, including fuzzy mathematics methods [20], gray correlation analysis [21,22], and extension identification methods provide new ideas for identifying water sources. Duan et al used the maximum likelihood method to estimate the mixing ratios of water sources at each site and analyze the evolution rules of mine water [23].…”

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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“…In addition, it is crucial to consider physicochemical information such as isotopes and radioactive elements in water bodies to reflect the essential characteristics and historical evolution of hydrogeology. The hydrogeochemical distribution, recharge sources, indicator tests, influencing factors, and evolutionary laws are analyzed based on conventional water chemistry, trace elements, and isotopes of the aquifer 8 . Gibbs' semi-qualitative model 9 is applied to the analysis of hydration types of surface water and shallow groundwater to reason about controlling factors, formation mechanisms, and recharge sources of isotopes in different aquifers, revealing the weathering and hydration types of different water bodies.…”

Section: Introductionmentioning

confidence: 99%

Identification of Mine Water Sources using a Multi-Dimensional Ion-Causative Nonlinear Algorithmic Model

Zheng

Liu

Yang

et al. 2023

Preprint

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This paper investigates the water quality system using the Piper diagram method to qualitatively analyze the distribution, characteristics, and laws of typical ion content. It also compares the differences between ion components of each aquifer with the closeness of the water body to be discriminated by combining statistical characteristic values. The R factor is utilized to simplify the index attributes, reduce input information dimension, and construct the coupled R-SVM discriminative model of Zhaogezhuang Mine. The input index is established as typical ion content, and the output is water source type. The discriminative model automatically establishes the mapping relationship between water quality indexes and discriminative criteria by learning the inherent property law between water quality samples. The independence components after dimensionality reduction are used as new discriminative indexes. The accuracy of the coupled model classification was 90.90% in the verification of the water source discrimination example of Zhaogezhuang mine. The coupled quantitative discriminant model based on R-factor and support vector machine provides an auxiliary verification and scientific decision for qualitative water chemistry analysis and provides a new idea for water source identification. Compared with the traditional qualitative way of water chemistry characteristics, Fisher function discrimination method, and single support vector machine model, this method improved the accuracy by more fully exploiting the internal laws of the data.

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“…It is the major energy source for electricity, residential heating, and industrial production in most countries (Qiao et al, 2019; Yin et al, 2019b). However, with the large-scale development of coal resources in China (Song et al, 2021; Yin et al, 2019a), especially with the increase and expansion of mining depth (Ruan et al, 2019), coal mining is bound to damage the surrounding rock structure of the aquifers (Shi et al, 2019a; Fan et al, 2019). Therefore, groundwater problems related to coal resource mining have become increasingly prominent.…”

Section: Introductionmentioning

confidence: 99%

“…Hydrogeochemical differences occur due to natural environmental processes by means of recharge and discharge, rock-water interaction, flow pattern, and residence times (Liu et al, 2018; Mu et al, 2018; Shi et al, 2019b). After accidental water inrush, it is crucial to quickly identify the water inrush source, find out the cause of the accident, and find mitigation strategies (Shi et al, 2019a; Song et al, 2021).…”

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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Identification model of water inrush source based on statistical analysis in Fengyu minefield, Northwest China

Cited by 9 publications

References 42 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

Identification of Mine Water Sources using a Multi-Dimensional Ion-Causative Nonlinear Algorithmic Model

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

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