Abstract
Longwall top coal caving mining is one of the main methods of mining thick coal seams in China. Therefore, carrying out the classification evaluation of top coal caving is of great significance to ensure mining success and reduce the risk of mining technology. In order to realize the classification evaluation of top coal caving, this article introduces the method of using BigML to establish the classification evaluation model of top coal caving. Furthermore, using the data from the CNKI database as sample data, a classification evaluation model of top coal caving is established on BigML. After training, testing, and optimization, the model is used to evaluate the top coal caving in No. 3 coal seam of Gucheng Coal Mine, and the evaluation result is grade 1, which is consistent with the engineering practice. The final research results show that the application of BigML in the classification evaluation of top coal caving is successful; the evaluation of top coal caving through BigML is reliable; BigML provides another scientific reliability way for the classification evaluation of top coal caving.
Highlights
According to the World Energy Statistics Review published in 2020, global coal consumption decreased by 0.6% in 2019, and the proportion of coal in primary energy reached the lowest level in 16 years, but the proportion of coal in primary energy is still up to 27% [1]. erefore, in the continuous development of other energy sources, coal is still one of the most critical energy sources [2], especially for a country such as China that is “rich in coal, poor in oil, and less in gas” and whose economy is developing rapidly, the status of coal is vital
Accuracy/error rate is the most commonly used indicator for researchers to evaluate the performance of classification prediction models, because they calculated the ratio of the number of correctly classified predictions to the total number of predictions and the number of incorrectly classified predictions accounted for the total number of predictions, and they can objectively reflect the global quality of the model
From the ROC AUC, PR AUC, and K-S values of each grade in the ROC curve (Figure 18), PR curve (Figure 19), and K-S curve (Figure 20) in model local performance evaluation parameters, the ROC AUC, PR AUC and K-S values of each grade of the classification evaluation model of top coal caving established by fusion are greater than or equal to that established by decision tree and depth network, respectively
Summary
According to the World Energy Statistics Review published in 2020, global coal consumption decreased by 0.6% in 2019, and the proportion of coal in primary energy reached the lowest level in 16 years, but the proportion of coal in primary energy is still up to 27% [1]. erefore, in the continuous development of other energy sources, coal is still one of the most critical energy sources [2], especially for a country such as China that is “rich in coal, poor in oil, and less in gas” and whose economy is developing rapidly, the status of coal is vital. Mohammadi et al [27] used fuzzy multicriteria decision-making methods to establish a classification system for evaluating the caving of the direct roof of coal seams; Yongkui et al [28] used Bayesian theory and rough set theory to establish a Bayesian classifier model used for the evaluation and prediction of roof caving properties of coal seams, which can accurately classify; Oraee and Rostami [29] used fuzzy logic algorithms to establish a fuzzy system for quantitative analysis of roof caving in longwall top coal caving mining face and applied the model to Tabas·Palward Mine’s longwall top coal caving mining face which located in Palward District, Yazd Province, and the model prediction results in application are consistent with the on-site measured results; Shi et al [17] established a top coal caving prediction model based on vector support vector machines, and the test results showed that the model has a certain feasibility and generalization; Yu and Mao [30] used SPSS statistical software to establish a top coal caving prediction model based on an artificial neural network. It is hoped that through this, it is possible to use the established model to evaluate the top coal caving without programming, and even modify and optimize the established model to make it more in line with their actual application situation
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