Identification of mixing water source and response mechanism of radium and radon under mining in limestone of coal seam floor

Huang, Pinghua; Gao, Hongfei; Su, Qiaoqiao; Zhang, Yanni; Cui, Mengke; Chai, Shuangwei; Li, Yuanmeng; Jiang, Y.

doi:10.1016/j.scitotenv.2022.159666

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…The conventional hydrochemical method serves as the primary means of analyzing the primary ion content, which includes indicators such as Ca 2+ , K + , Na + , SO 4 2− , HCO − , Mg 2+ , Cl − , CO 3 2− , dissolved oxygen, alkalinity, acidity, pH value, and mineralization degree. This method plays a crucial role in determining the water quality classification of both the aquifer and the water influx [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Additionally, it proves effective in identifying a single water source characterized by significant differences in water quality attributes within the aquifer.…”

Section: Introductionmentioning

confidence: 99%

Discrimination Methods of Mine Inrush Water Source

Dong,

Zhang

2023

Water

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Ensuring mining safety and efficiency relies heavily on identifying the source of mine water inrush. This review article aims to provide a comprehensive overview of standard methods used to pinpoint the origin of mine water inrush, highlighting the development and progress in the research of discrimination methods. These methods are systematically classified into various categories, encompassing hydrochemistry examination, water level and temperature analysis, geostatistical approaches, machine learning and deep learning methods, as well as the utilization of other analytical techniques. The review not only presents a quantitative and visual analysis of the theoretical methods proposed by scholars but also emphasizes their strengths, weaknesses, and applicability to various mining operations. Furthermore, it explores the increasing utilization of artificial neural networks and machine learning algorithms in source discrimination models, indicating the advancement in this area of research. To further advance the field, specific examples of these methods and their effectiveness in identifying the source of mine water inrush are provided, aiming to stimulate further research. The article also offers detailed recommendations for future research directions and emerging trends, underlining the importance of comprehensive multidisciplinary and multi-method analysis. It suggests exploring emerging technologies such as the Internet of Things (IoT) and cloud computing, while emphasizing the need to develop more accurate and reliable models for source identification. The fusion of artificial intelligence (AI), heightened computational capabilities, online programming, and intelligent data collection systems presents the prospect of transforming the way industries respond to these critical events. By providing a comprehensive overview, analyzing the effectiveness of existing methods, and proposing future research directions, this review aims to contribute to the continuous development and progress of discrimination methods for mine water inrush incidents. Ultimately, it seeks to enhance mining safety and efficiency by facilitating the prompt and accurate identification of the sources of mine water inrush.

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Section: Introductionmentioning

confidence: 99%

Discrimination Methods of Mine Inrush Water Source

Dong,

Zhang

2023

Water

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“…Combining machine learning methods with water chemistry ions to construct discriminant models is necessary. Many scholars have achieved fruitful results by introducing machine learning methods into the identification of mine water sources, such as Support Vector Machines, Naive Bayes 15 , Back Propagation Neural Networks 16 , Logistic Regression Analysis 17 , and Random Forest 18,19 . All of the above methods can solve most practical problems.…”

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confidence: 99%

Identification of mine water source based on TPE-LightGBM

Wang,

Zhang,

et al. 2024

Sci Rep

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Mine water inrush is a serious threat to mine safety production. It is very important to identify water inrush source types quickly to prevent and control water damage. In this study, the aqueous chemical components Na+ + K+, Ca2+, Mg2+, Cl−, SO42− and HCO3− of different aquifers in Pingdingshan coalfield were selected as the characteristic values, and the Surface water, Quaternary pore water, Carboniferous limestone karst water, Permian sandstone water, and Cambrian limestone karst water were used as the labels. An intelligent water source discrimination model is proposed by combining data mining, classification models, and reinforcement learning. As outlier data in the samples may interfere with the model recognition ability, the data distribution range was analyzed using box plots, and 20 groups of abnormal samples were excluded. The processed water chemistry data were divided into 80% learning samples and 20% test samples, and the learning samples were fed into a light gradient boosting machine (LightGBM) for training. The tree-structured parson estimator (TPE) obtains the optimal values of the main parameters of LightGBM in a very short time. Substituting the hyperparameters back into the model yields a 13.9% improvement in the accuracy of the model, proving the effectiveness of the TPE algorithm. To further validate the performance of the model, TPE-LightGBM is compared and analyzed with a Random Search-Multi Layer Perceptron Machine (RS-MLP) and Genetic Algorithm-Extreme Gradient Boosting Tree (GA-SVM). The accuracy of TPE-LightGBM, RS-MLP, and GA-SVM is 0.931, 0.759, 0.724 in that order, and the generalization error RMSE is 0.415, 1.05, and 1.313 in that order. The results show that TPE-LightGBM is more advantageous in water source identification and is more resistant to overfitting. By calculating and comparing the information gain of each variable, the contribution of Ca2+ is the highest, so it is necessary to pay attention to the change in Ca2+ concentration. TPE-LightGBM’s high accuracy and generalization ability have a good prospect for the identification of sudden water source types.

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Numerical investigation on the effects of axial-stress loads on the temperature-programmed oxidation characteristics of loose broken coal

Yu,

Yang,

et al. 2024

Energy

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Identification of mixing water source and response mechanism of radium and radon under mining in limestone of coal seam floor

Cited by 6 publications

References 15 publications

Discrimination Methods of Mine Inrush Water Source

Discrimination Methods of Mine Inrush Water Source

Identification of mine water source based on TPE-LightGBM

Numerical investigation on the effects of axial-stress loads on the temperature-programmed oxidation characteristics of loose broken coal

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