Prediction of water inrush from coal seam floor based on machine learning with small sample data
With the development of computer technology, machine learning method has become an important technology for the prediction of water inrush in coal seam floor. However, the prediction accuracy of many machine learning algorithms requires a high number of samples, which restricts the practical applica...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Safety in Coal Mines
2025-01-01
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| Series: | Meikuang Anquan |
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| Online Access: | https://www.mkaqzz.com/cn/article/doi/10.13347/j.cnki.mkaq.20231371 |
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| author | Chenxi LI Haifeng LU |
| author_facet | Chenxi LI Haifeng LU |
| author_sort | Chenxi LI |
| collection | DOAJ |
| description | With the development of computer technology, machine learning method has become an important technology for the prediction of water inrush in coal seam floor. However, the prediction accuracy of many machine learning algorithms requires a high number of samples, which restricts the practical application. In this paper, by using the nearest neighbor algorithm (KNN) and the combination algorithm of gradient boosting decision tree (GBDT) and logistic regression (LR), a water inrush prediction model was established based on the sample data of six indexes, including water pressure, mining height, water-barrier thickness, fault drop, coal seam inclination, and fault distance from the working face. The influence rule of sample number on prediction accuracy was discussed, and the comparison study was conducted with the commonly used particle swarm, support vector machine, BP neural network, random forest and convolutional neural network. The results show that when the number of samples reaches 18, the prediction accuracy of KNN and GBDT+LR remains stable. The prediction accuracy of KNN and GBDT+LR is higher than that of conventional models under small sample conditions. The predicted results of the model agree with the actual situation. |
| format | Article |
| id | doaj-art-f411ae06489b4f15a3b4c7f637ec86f2 |
| institution | Kabale University |
| issn | 1003-496X |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Office of Safety in Coal Mines |
| record_format | Article |
| series | Meikuang Anquan |
| spelling | doaj-art-f411ae06489b4f15a3b4c7f637ec86f22025-01-15T04:32:08ZzhoEditorial Office of Safety in Coal MinesMeikuang Anquan1003-496X2025-01-0156117117910.13347/j.cnki.mkaq.20231371gMKAQ20231371Prediction of water inrush from coal seam floor based on machine learning with small sample dataChenxi LI0Haifeng LU1School of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, ChinaSchool of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, ChinaWith the development of computer technology, machine learning method has become an important technology for the prediction of water inrush in coal seam floor. However, the prediction accuracy of many machine learning algorithms requires a high number of samples, which restricts the practical application. In this paper, by using the nearest neighbor algorithm (KNN) and the combination algorithm of gradient boosting decision tree (GBDT) and logistic regression (LR), a water inrush prediction model was established based on the sample data of six indexes, including water pressure, mining height, water-barrier thickness, fault drop, coal seam inclination, and fault distance from the working face. The influence rule of sample number on prediction accuracy was discussed, and the comparison study was conducted with the commonly used particle swarm, support vector machine, BP neural network, random forest and convolutional neural network. The results show that when the number of samples reaches 18, the prediction accuracy of KNN and GBDT+LR remains stable. The prediction accuracy of KNN and GBDT+LR is higher than that of conventional models under small sample conditions. The predicted results of the model agree with the actual situation.https://www.mkaqzz.com/cn/article/doi/10.13347/j.cnki.mkaq.20231371water inrush from floormining water disastermine inflowmachine learningwater inrush prediction |
| spellingShingle | Chenxi LI Haifeng LU Prediction of water inrush from coal seam floor based on machine learning with small sample data Meikuang Anquan water inrush from floor mining water disaster mine inflow machine learning water inrush prediction |
| title | Prediction of water inrush from coal seam floor based on machine learning with small sample data |
| title_full | Prediction of water inrush from coal seam floor based on machine learning with small sample data |
| title_fullStr | Prediction of water inrush from coal seam floor based on machine learning with small sample data |
| title_full_unstemmed | Prediction of water inrush from coal seam floor based on machine learning with small sample data |
| title_short | Prediction of water inrush from coal seam floor based on machine learning with small sample data |
| title_sort | prediction of water inrush from coal seam floor based on machine learning with small sample data |
| topic | water inrush from floor mining water disaster mine inflow machine learning water inrush prediction |
| url | https://www.mkaqzz.com/cn/article/doi/10.13347/j.cnki.mkaq.20231371 |
| work_keys_str_mv | AT chenxili predictionofwaterinrushfromcoalseamfloorbasedonmachinelearningwithsmallsampledata AT haifenglu predictionofwaterinrushfromcoalseamfloorbasedonmachinelearningwithsmallsampledata |