Source Discrimination of Mine Water Inrush Using Multiple Combinations of an Improved Support Vector Machine Model

Wei, Zhonglin; Dong, Donglin; Ji, Yuan; Ding, Jia; Yu, Lujia

doi:10.1007/s10230-022-00884-5

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…Support vector machine (SVM), a statistical learning model, erects an optimal hyperplane in the sample space to achieve superior generalization ability and prediction accuracy, well-suited for small sample data [65]. Wei [65] amalgamated several algorithms including the Fisher identification method, self-organizing correlation method (SOM), improved principal component analysis method (PCSOM), and the grey wolf algorithm (GWO)optimized support vector machine (GWOSVM). The results demonstrated the efficacy of the PCSOM algorithm in diminishing information overlap between discriminant indices, streamlining model structure, and refining algorithm efficiency.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

“…Visionaries from disparate corners of the world have wholeheartedly embraced interdisciplinary methodologies akin to those espoused in antecedent research [65]. By amalgamating artificial neural networks and computational algorithms with insights gleaned from domains such as biology and computer science, savants have directed their focus toward the augmentation of water origin demarcation efficacy.…”

Section: Field Applicationsmentioning

confidence: 99%

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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: Machine Learning Methodsmentioning

confidence: 99%

Section: Field Applicationsmentioning

confidence: 99%

Discrimination Methods of Mine Inrush Water Source

Dong,

Zhang

2023

Water

Self Cite

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“…Over the past few years, the expansion of coal mining operations to deeper areas of the mine has increased the risk of water inrush accidents caused by changes in ground stress and rock fractures that enhance infiltration. Therefore, it is crucial to promptly and accurately identify the type of water source and the water inrush channel to prevent water damage 1,2,3 . Doing so can offer a scientific basis for swift rescue and treatment.…”

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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“…Water source discrimination can be seen as a classification problem [11][12][13][14]. Many scholars have achieved excellent results by incorporating machine learning classification methods, such as multiple logistic regression [15], Bayesian networks [16], the artificial neural network [17], the support vector machine [18], and so on, into the mine water discrimination problem, and the methods described above can solve the majority of the practical problems. Nevertheless, when the hydrological circumstances in the study area are complex and many types of water samples are intermingled, Bayesian networks cannot match the demand for numerous classifications.…”

Section: Introductionmentioning

confidence: 99%

Classification of Water Source in Coal Mine Based on PCA-GA-ET

Yang

Wang

et al. 2023

Water

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In recent years, inrush water has hampered the regular mining of coal mines, and the proper identification of the source of inrush water is critical to the prevention and management of water hazards in mines. This paper extracts the standard water chemistry discriminating ions Na++K+, Ca2+, Mg2+, Cl−, SO42−, and HCO3− from observed water samples. An improved water source discrimination model is proposed which combines algorithms from data mining, classification models, and learning reinforcement. According to the Pearson correlation coefficient, Na++K+ has a strong correlation with HCO3−. To identify the major metrics, we performed principal component analysis (PCA), and the adaptive differential evolutionary genetic algorithm (GA) was utilized to optimize the depth of the extreme tree (ET) and the number of classifiers. Finally, the model distinguished 25 sets of studied samples from various water sources in the Pingdingshan coalfield. Comparative analysis demonstrated the efficacy of each stage of our work. PCA-GA-ET outperformed the conventional approaches, such as the support vector machine, BP artificial neural network, and random forest. The studies revealed that PCA-GA-ET can eliminate the information overlap between data and simplify the data structure and thereby improve the efficiency and accuracy of water source detection. We discovered that by utilizing the evolutionary algorithm to optimize parameters such as the depth of the extreme trees and the number of decision trees, we could get the model to converge faster and to be more stable and more accurate. The results suggest that PCA-GA-ET has good robustness and accuracy and can meet the needs of water source identification.

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Source Discrimination of Mine Water Inrush Using Multiple Combinations of an Improved Support Vector Machine Model

Cited by 10 publications

References 14 publications

Discrimination Methods of Mine Inrush Water Source

Discrimination Methods of Mine Inrush Water Source

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

Classification of Water Source in Coal Mine Based on PCA-GA-ET

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