Evaluation and prediction of coal seam mining mode: Coefficient of Variation-TOPSIS and CNN-NGO methods
Selecting the appropriate method for coal seam mining is the foremost task for the safe and efficient production of coal mines. This study explores and validates an integrated evaluation system that enhances the accuracy of predicting coal seam mining mode by comparing traditional evaluation methods...
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-01-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0243609 |
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| author | Haixiong Li Fei Wang |
| author_facet | Haixiong Li Fei Wang |
| author_sort | Haixiong Li |
| collection | DOAJ |
| description | Selecting the appropriate method for coal seam mining is the foremost task for the safe and efficient production of coal mines. This study explores and validates an integrated evaluation system that enhances the accuracy of predicting coal seam mining mode by comparing traditional evaluation methods with machine-learning techniques. The weights of the evaluation indicators for coal seam mining were allocated using the coefficient of variation method, followed by a comprehensive evaluation using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In addition, a Convolutional Neural Network (CNN) regression model was constructed and optimized with the Northern Goshawk Optimization (NGO) algorithm, resulting in a more precise CNN-NGO prediction model. A detailed comparative analysis of these two models was conducted. The results indicate that the level of equipment is a key factor affecting the method of coal seam mining, holding the highest weight among all evaluation indicators. The CNN-NGO model demonstrates excellent performance in predicting coal seam mining mode, with its predictions highly consistent with actual mining practices. Specifically, in the practical application case of the WJZ 15206 working face, the model successfully predicted the high mining height as the most likely mining method, with a prediction index of 2.4265, close to the normalized output value of 2 for large mining height. This study not only provides scientific methods and tools for the evaluation and prediction of coal seam mining mode but also contributes to the intelligent development of the coal mining industry. |
| format | Article |
| id | doaj-art-6988e44572cd4e779e11ef80eb644460 |
| institution | Kabale University |
| issn | 2158-3226 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-6988e44572cd4e779e11ef80eb6444602025-02-03T16:40:42ZengAIP Publishing LLCAIP Advances2158-32262025-01-01151015129015129-1110.1063/5.0243609Evaluation and prediction of coal seam mining mode: Coefficient of Variation-TOPSIS and CNN-NGO methodsHaixiong Li0Fei Wang1Department of Law Enforcement Supervision, Energy Bureau of Yulin, Yulin, Shaanxi Province 719000, ChinaGuoneng Shendong Coal Group Co., LTD., Yulin, Shaanxi Province 719315, ChinaSelecting the appropriate method for coal seam mining is the foremost task for the safe and efficient production of coal mines. This study explores and validates an integrated evaluation system that enhances the accuracy of predicting coal seam mining mode by comparing traditional evaluation methods with machine-learning techniques. The weights of the evaluation indicators for coal seam mining were allocated using the coefficient of variation method, followed by a comprehensive evaluation using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In addition, a Convolutional Neural Network (CNN) regression model was constructed and optimized with the Northern Goshawk Optimization (NGO) algorithm, resulting in a more precise CNN-NGO prediction model. A detailed comparative analysis of these two models was conducted. The results indicate that the level of equipment is a key factor affecting the method of coal seam mining, holding the highest weight among all evaluation indicators. The CNN-NGO model demonstrates excellent performance in predicting coal seam mining mode, with its predictions highly consistent with actual mining practices. Specifically, in the practical application case of the WJZ 15206 working face, the model successfully predicted the high mining height as the most likely mining method, with a prediction index of 2.4265, close to the normalized output value of 2 for large mining height. This study not only provides scientific methods and tools for the evaluation and prediction of coal seam mining mode but also contributes to the intelligent development of the coal mining industry.http://dx.doi.org/10.1063/5.0243609 |
| spellingShingle | Haixiong Li Fei Wang Evaluation and prediction of coal seam mining mode: Coefficient of Variation-TOPSIS and CNN-NGO methods AIP Advances |
| title | Evaluation and prediction of coal seam mining mode: Coefficient of Variation-TOPSIS and CNN-NGO methods |
| title_full | Evaluation and prediction of coal seam mining mode: Coefficient of Variation-TOPSIS and CNN-NGO methods |
| title_fullStr | Evaluation and prediction of coal seam mining mode: Coefficient of Variation-TOPSIS and CNN-NGO methods |
| title_full_unstemmed | Evaluation and prediction of coal seam mining mode: Coefficient of Variation-TOPSIS and CNN-NGO methods |
| title_short | Evaluation and prediction of coal seam mining mode: Coefficient of Variation-TOPSIS and CNN-NGO methods |
| title_sort | evaluation and prediction of coal seam mining mode coefficient of variation topsis and cnn ngo methods |
| url | http://dx.doi.org/10.1063/5.0243609 |
| work_keys_str_mv | AT haixiongli evaluationandpredictionofcoalseamminingmodecoefficientofvariationtopsisandcnnngomethods AT feiwang evaluationandpredictionofcoalseamminingmodecoefficientofvariationtopsisandcnnngomethods |