CEEMD: A New Method to Identify Mine Water Inrush Based on the Signal Processing and Laser-Induced Fluorescence

Bian, Kai; Zhou, Mengran; Hu, Feng; Lai, Wenhao; Huang, Man

doi:10.1109/access.2020.3000333

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…The management of mine water hazards typically begins at the source of water inrush, and determining the type of water inrush and the cause of water inrush correctly and efficiently is a critical stage in the prevention and control of water inrush in mines [3]. In recent studies, the methods used to determine the source of water inrush in mines have mainly included the water temperature and level method, laser-induced fluorescence method [4,5], and water chemical analysis method. One discriminative strategy is to develop discriminative functions for various aquifers based on the otherness of geochemical ion concentrations in aquifers combined with mathematical methods [6].…”

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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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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“…Water source discrimination works on the premise of controlling mine water inrush, and accurate and efficient identification of water inrush sources is of great importance for mine water disaster prevention. In recent years, standard water source identification methods include water temperature and water level method 1 , LIF laser-induced fluorescence method [2][3][4][5][6] , water chemical analysis method, etc. The research shows that the hydrochemistry method is the most commonly used discrimination method at present.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of water inrush source in coal mine based on PCA-GA-ET

Lv¹,

Yang²,

Yan³

et al. 2022

Preprint

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In recent years, mine water inrush has hindered coal mining, and accurate identification of the source of water inrush is essential for the prevention and control of mine water disasters. To study the source of water inrush in Pingdingshan coalfield, water source samples of 124 groups of main water inrush aquifers were collected. The main water-filled aquifers in Pingdingshan coalfield include surface water (I), Quaternary pore water (II), Carboniferous limestone karst water (III), Permian sandstone water (IV) and Cambrian limestone karst water (IV).In this paper, the conventional hydrochemical discriminant ions Na++K+, Ca2+, Mg2+, Cl-, SO42-, and HCO3- of observed water samples are extracted to build a water source discriminant model. According to the Pearson correlation coefficient, Na++K+has a strong correlation with HCO3-. To avoid the impact of information overlap on the accuracy of the model, principal component analysis (PCA) is used to extract the main indicators. Based on the adaptive differential evolution genetic algorithm (GA) to optimize the super parameters of the extreme tree (ET), the PCA-GA-ET water source identification model is constructed. To further verify the reliability of the model, PCA-GA-ET is compared with grid search-random forest (GS-RF), artificial neural network (MLP), and particle swarm optimization-support vector machine (PSO-SVM). Finally, using the trained model to discriminate 25 groups of validation samples, it can be concluded that the discrimination results of 24 groups of samples are consistent with the observation results, and the accuracy of PCA-GA-ET is 96%. Research shows that the use of principal component analysis can reduce the information overlap of data, and the extreme tree (ET) model optimized by genetic algorithm (GA) greatly improves the efficiency and accuracy of water inrush source identification.

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“…With the significant improvement of computer performance, the machine learning algorithm has been used in many fields widely [12]- [15]. The regression algorithm is a typical classification in the machine learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

Compensation Method for Shadow Effect of Mine Ultrasonic Anemometer

Cao

Zhang

et al. 2021

IEEE Access

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The ultrasonic anemometer is widely used in the field of wind speed measurement in the coal mine. To compensate for the error of wind speed caused by the shadow effect, we propose a method based on kernel extreme learning machine combined with particle swarm optimization (PSO-KELM). Firstly, we established and analyzed the simulation experiment by computational fluid dynamics (CFD). Secondly, we used the PSO to optimize the parameters of the KELM. The evaluation indexes of the PSO-KELM on the test set are better than other regression models. Finally, the accuracy of the PSO-KELM was verified by experimental data obtained by the ultrasonic anemometer in the wind tunnel. The results show that the compensation effect of the PSO-KELM is better than other regression models. The PSO-KELM model proposed in the paper can effectively compensate for the error of wind speed, and make the measurement results of the ultrasonic anemometer more accurate.INDEX TERMS ultrasonic anemometer, coal mine, shadow effect, kernel extreme learning machine, error compensation.

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CEEMD: A New Method to Identify Mine Water Inrush Based on the Signal Processing and Laser-Induced Fluorescence

Cited by 8 publications

References 38 publications

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

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

Discrimination of water inrush source in coal mine based on PCA-GA-ET

Compensation Method for Shadow Effect of Mine Ultrasonic Anemometer

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