Research on prediction of support parameters for coal roadways
Currently, algorithms such as support vector machine (SVM) and random forest (RF) are less applied in the field of coal mine roadway support. The paper studies the applicability of different machine learning models for support parameter design.Thus a higher performance model would be established to...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Industry and Mine Automation
2023-10-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2022120047 |
| _version_ | 1797563579436630016 |
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| author | CHEN Pan MA Xinmin XIANG Junjie CHEN Liying LIANG Tinghao |
| author_facet | CHEN Pan MA Xinmin XIANG Junjie CHEN Liying LIANG Tinghao |
| author_sort | CHEN Pan |
| collection | DOAJ |
| description | Currently, algorithms such as support vector machine (SVM) and random forest (RF) are less applied in the field of coal mine roadway support. The paper studies the applicability of different machine learning models for support parameter design.Thus a higher performance model would be established to achieve reasonable and scientific design of anchor bolt support. Firstly, it is suggested to establish an intelligent prediction database for coal mine roadway support. Through on-site research, questionnaire survey, and literature search, the coal mine roadway samples are collected. The data is processed using methods such as filling in missing values, modifying outliers in box charts, and removing local abnormal factors to establish a coal roadway support database. The paper proposes a coal roadway support parameter prediction model based on synthetic minority oversampling technique (SMOTE) - genetic algorithm (GA) - SVM. The data in the database is divided into training and testing sets. The SMOTE technology is used to balance training samples, and improve the model's fitting capability for minority class samples. The training process uses GA to globally optimize the hyperparameters of SVM, further improving the overall performance of the model. The test results show that the classificaton precision of the SMOTE-GA-SVM model reaches 83.8%, which is 21.8% higher than the traditional SVM model. The machine learning methods such as SVM, artificial neural network (ANN), RF, AdaBoost (ADA), and naive Bayesian classifier (NBC) are introduced into the prediction of coal roadway anchor support parameters. The corresponding support parameter prediction models are established. The comparison results showed that the best to worst prediction models are ranked as SMOTE-GA-SVM, RF, GA-ANN, SVM, NBC, and ADA, with an average classificaton precision of 69.9%. The result verifies the feasibility of machine learning methods in predicting the parameters of coal roadway bolt support. The SMOTE-GA-SVM model is applied in Shanxi Huobaoganhe Coal Mine Co., Ltd., with a precision of 87.5% and strong applicability and reliability. |
| first_indexed | 2024-03-10T18:45:35Z |
| format | Article |
| id | doaj.art-7da039e77b714de298d38214b53e947d |
| institution | Directory Open Access Journal |
| issn | 1671-251X |
| language | zho |
| last_indexed | 2024-03-10T18:45:35Z |
| publishDate | 2023-10-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj.art-7da039e77b714de298d38214b53e947d2023-11-20T05:32:18ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2023-10-01491013314110.13272/j.issn.1671-251x.2022120047Research on prediction of support parameters for coal roadwaysCHEN PanMA Xinmin0XIANG Junjie1CHEN Liying2LIANG Tinghao3School of Mechanics and Civil Engineering, China University of Mining and Technology-Beijing, Beijing 100083, ChinaSchool of Mechanics and Civil Engineering, China University of Mining and Technology-Beijing, Beijing 100083, ChinaSchool of Mechanics and Civil Engineering, China University of Mining and Technology-Beijing, Beijing 100083, ChinaSchool of Mechanics and Civil Engineering, China University of Mining and Technology-Beijing, Beijing 100083, ChinaCurrently, algorithms such as support vector machine (SVM) and random forest (RF) are less applied in the field of coal mine roadway support. The paper studies the applicability of different machine learning models for support parameter design.Thus a higher performance model would be established to achieve reasonable and scientific design of anchor bolt support. Firstly, it is suggested to establish an intelligent prediction database for coal mine roadway support. Through on-site research, questionnaire survey, and literature search, the coal mine roadway samples are collected. The data is processed using methods such as filling in missing values, modifying outliers in box charts, and removing local abnormal factors to establish a coal roadway support database. The paper proposes a coal roadway support parameter prediction model based on synthetic minority oversampling technique (SMOTE) - genetic algorithm (GA) - SVM. The data in the database is divided into training and testing sets. The SMOTE technology is used to balance training samples, and improve the model's fitting capability for minority class samples. The training process uses GA to globally optimize the hyperparameters of SVM, further improving the overall performance of the model. The test results show that the classificaton precision of the SMOTE-GA-SVM model reaches 83.8%, which is 21.8% higher than the traditional SVM model. The machine learning methods such as SVM, artificial neural network (ANN), RF, AdaBoost (ADA), and naive Bayesian classifier (NBC) are introduced into the prediction of coal roadway anchor support parameters. The corresponding support parameter prediction models are established. The comparison results showed that the best to worst prediction models are ranked as SMOTE-GA-SVM, RF, GA-ANN, SVM, NBC, and ADA, with an average classificaton precision of 69.9%. The result verifies the feasibility of machine learning methods in predicting the parameters of coal roadway bolt support. The SMOTE-GA-SVM model is applied in Shanxi Huobaoganhe Coal Mine Co., Ltd., with a precision of 87.5% and strong applicability and reliability.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2022120047coal mine roadwaymachine learninganchor rod support parametersoversampling of synthesized minority classesgenetic algorithm optimizing support vector machine |
| spellingShingle | CHEN Pan MA Xinmin XIANG Junjie CHEN Liying LIANG Tinghao Research on prediction of support parameters for coal roadways Gong-kuang zidonghua coal mine roadway machine learning anchor rod support parameters oversampling of synthesized minority classes genetic algorithm optimizing support vector machine |
| title | Research on prediction of support parameters for coal roadways |
| title_full | Research on prediction of support parameters for coal roadways |
| title_fullStr | Research on prediction of support parameters for coal roadways |
| title_full_unstemmed | Research on prediction of support parameters for coal roadways |
| title_short | Research on prediction of support parameters for coal roadways |
| title_sort | research on prediction of support parameters for coal roadways |
| topic | coal mine roadway machine learning anchor rod support parameters oversampling of synthesized minority classes genetic algorithm optimizing support vector machine |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2022120047 |
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