Hydro-chemical evolution of groundwater and mixing between aquifers: a statistical approach based on major ions

Sun, Linhua; Gui, Herong

doi:10.1007/s13201-014-0169-7

Cited by 28 publications

(14 citation statements)

References 20 publications

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“…Discriminant analysis: it uses linear or quadratic functions of the variables that are "best" divided into two or more predefined groups in a separate case. It is a simple and effective tool for the establishment of the inrush water source identification model (Sun and Gui 2015). 5.…”

Section: Statistical Analysesmentioning

confidence: 99%

“…Groundwater in the aquifers contains a series of information, including the water level, quantity, temperature, velocity, turbidity and hydrochemistry, among others (Gui and Chen 2007), which provides the possibility of the water source identification among different aquifers, and a large number of methods have been proposed (Liu et al 2018;Shi et al 2018;Wu et al 2019;Wang et al 2020). For example, the mixing ratio of water inrush sources in the Qidong coal mine, Anhui province, China, has been calculated by factor analysis, and results provide theoretical guidance for the prevention and control of water disasters (Sun and Gui 2015). Moreover, the source of inrush water had been identified by means of Q-model and principal component analysis in Zhaizhen coal mine, Shandong Province, China (Wang and Shi 2019).…”

Section: Introductionmentioning

confidence: 99%

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Statistical analyses of groundwater chemistry in the Qingdong coalmine, northern Anhui province, China: implications for water–rock interaction and water source identification

Chen

Sun

Xu³

2021

Appl Water Sci

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Hydrochemistry of groundwater is important in coal mines because it can be used for understanding water–rock interaction and inrush water source identification. In this study, major ion concentrations of groundwater samples from the loose layer aquifer (LA), coal-bearing aquifer (CA) and Taiyuan Formation limestone aquifer (TA) in the Qingdong coal mine, northern Anhui province, China, have been analyzed by a series of statistical methods for identifying the source of chemical constituents in groundwater and the source of inrush water. The results indicate that the mean concentration of the major ions in the LA were ordered as follows: HCO3− > SO42− > Na+ + K+ > Cl− > Ca2+ > Mg2+ > CO32−, whereas average values of the CA in decreasing order are SO42−, Na+ + K+, HCO3−, Cl−, Ca2+, Mg2+ and CO32−, and the major ion concentrations of the TA have the following order: SO42− > Na+ + K+ > Ca2+ > HCO3− > Cl− > Mg2+ > CO32−, and most of the samples are Na-SO4 and Ca-SO4 types. TDS content in water increases with aquifer depth, whereas the pH values ranged from 7.1 to 8.9, indicating a weak alkaline environment. Two sources (weathering of silicate minerals and dissolution of evaporate minerals) have been identified by principal component analysis responsible for the chemical variations of the groundwater, and their contribution ratios have been quantified by Unmix model. Moreover, based on the Q-mode cluster and discriminant analyses, the samples with known sources have been identified correctly to be 95.7% and 97.6%, respectively, and the samples with unknown sources have been determined with high probability (78–100%).

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Section: Statistical Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical analyses of groundwater chemistry in the Qingdong coalmine, northern Anhui province, China: implications for water–rock interaction and water source identification

Chen

Sun

Xu³

2021

Appl Water Sci

Self Cite

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“…However, discriminant model based on data driven is an important approach to solve the problem of discrimination of water type in mines nowadays. Related methods include the support vector machine (SVM), 9 clustering analysis, 10 extreme learning machine (ELM), 3 back propagating neural network, 11 and so on. Gradually, these methods are applied to discriminate the type of water source in mine.…”

Section: Introductionmentioning

confidence: 99%

Generative adversarial nets in laser-induced fluorescence spectrum image recognition of mine water inrush

Yang

et al. 2019

International Journal of Distributed Sensor Networks

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Water inrush occurred in mines, threatens the safety of working miners which triggers severe accidents in China. To make full use of existing distinctive hydro chemical and physical characteristics of different aquifers and different water sources, this article proposes a new water source discrimination method using laser-induced fluorescence technology and generative adversarial nets. The fluorescence spectrum from the water sample is stimulated by 405-nm lasers and improved by recursive mean filtering method to alleviate interference and auto-correlation to enhance the feature difference. Based on generative adversarial nets framework and improved spectra features, the article proposes a novel water source discrimination-generative adversarial nets model in mines to solve the problem of data limitation and improve the discrimination ability. The results show that the proposed method is an effective method to distinguish water inrush types. It provides a new idea to discriminate the sources of water inrush in mines timely and accurately.

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“…Due to the complexity of groundwater pollution, the assessment processes are divided into a single factor evaluation and comprehensive factor evaluation. Comprehensive factor evaluation methods are used widely such as the comprehensive index method, fuzzy mathematics method, grey clustering method, and artificial neural network method [10][11][12][13][14][15]. Mehra [16] evaluated groundwater quality with the weighted index method and proposed an integrated assessment of groundwater for agricultural use using a geographical information system (GIS), resulting in some important and useful results that decision makers could apply to measures supporting sustainable groundwater resource management in the study area.…”

Section: Introductionmentioning

confidence: 99%

Hydrochemical Characteristics and Groundwater Quality Assessment in the Diluvial Fan of Gaoqiao, Emei Mountain, China

Yang

Chen

et al. 2018

Sustainability

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Methods for groundwater quality and pollution assessment are applied extensively, but it is difficult to determine a unified evaluation model. On the basis of hydrogeochemical characteristics analysis in 2016 compared with that in 1995, the five-element connection number SPA (set pair analysis) method was applied to evaluate the groundwater quality of the Gaoqiao diluvial fan under the influence of hydrogeological conditions and human activities along the flow path in our work. Descriptive statistics methods, Piper diagram, and a Schoeller diagram were also used to analyze the hydrochemical characteristics of groundwater such as chemical components, total dissolved solid, and total hardness. The change of the typical pollutant of fluorine was analyzed to evaluate the groundwater quality under the influence of human activities. The results showed that the groundwater quality in the study area was more in rank П. The basic hydrochemical types of shallow groundwater were HCO3-Ca·Mg and HCO3·SO4-Ca·Mg. The influencing factors of the hydrochemical component of groundwater were identified in the Gaoqiao diluvial fan. The quality of groundwater changed slightly from the top to the edge of the fan due to the water–rock interaction except for in Yucun and Hucun influenced by human activities. The assessment result can provide a scientific basis for the pollution prevention and changing process control of the groundwater in the hydrogeological unit of the Gaoqiao diluvial fan.

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Hydro-chemical evolution of groundwater and mixing between aquifers: a statistical approach based on major ions

Cited by 28 publications

References 20 publications

Statistical analyses of groundwater chemistry in the Qingdong coalmine, northern Anhui province, China: implications for water–rock interaction and water source identification

Statistical analyses of groundwater chemistry in the Qingdong coalmine, northern Anhui province, China: implications for water–rock interaction and water source identification

Generative adversarial nets in laser-induced fluorescence spectrum image recognition of mine water inrush

Hydrochemical Characteristics and Groundwater Quality Assessment in the Diluvial Fan of Gaoqiao, Emei Mountain, China

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