Prediction and Evaluation of Coal Mine Coal Bump Based on Improved Deep Neural Network
Coal bump prediction is one of the key problems in deep coal mining engineering. To predict coal bump disaster accurately and reliably, we propose a depth neural network (DNN) prediction model based on the dropout method and improved Adam algorithm. The coal bump accident examples were counted in or...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2021/7794753 |
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| _version_ | 1832549662068834304 |
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| author | Shuang Gong Yi Tan Wen Wang |
| author_facet | Shuang Gong Yi Tan Wen Wang |
| author_sort | Shuang Gong |
| collection | DOAJ |
| description | Coal bump prediction is one of the key problems in deep coal mining engineering. To predict coal bump disaster accurately and reliably, we propose a depth neural network (DNN) prediction model based on the dropout method and improved Adam algorithm. The coal bump accident examples were counted in order to analyze the influencing factors, characteristics, and causes of this type of accidents. Finally, four indexes of maximum tangential stress of surrounding rock, uniaxial compressive strength of rock, uniaxial tensile strength of rock, and elastic energy of rock are selected to form the prediction index system of coal bump. Based on the research results of rock burst, 305 groups of rock burst engineering case data are collected as the sample data of coal bump prediction, and then, the prediction model based on a dropout and improved Adam-based deep neural network (DA-DNN) is established by using deep learning technology. The DA-DNN model avoids the problem of determining the index weight, is completely data-driven, reduces the influence of human factors, and can realize the learning of complex and subtle deep relationships in incomplete, imprecise, and noisy limited data sets. A coal mine in Shanxi Province is used to predict coal bump with the improved depth learning method. The prediction results verify the effectiveness and correctness of the DA-DNN coal bump prediction model. Finally, it is proved that the model can effectively provide a scientific basis for coal bump prediction of similar projects. |
| format | Article |
| id | doaj-art-7b2de208e55141fdbc70b3a4d411c90d |
| institution | Kabale University |
| issn | 1468-8115 1468-8123 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-7b2de208e55141fdbc70b3a4d411c90d2025-02-03T06:10:47ZengWileyGeofluids1468-81151468-81232021-01-01202110.1155/2021/77947537794753Prediction and Evaluation of Coal Mine Coal Bump Based on Improved Deep Neural NetworkShuang Gong0Yi Tan1Wen Wang2State Key Laboratory of Water Resource Protection and Utilization in Coal Mining, Beijing 100011, ChinaState Key Laboratory of Water Resource Protection and Utilization in Coal Mining, Beijing 100011, ChinaSchool of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaCoal bump prediction is one of the key problems in deep coal mining engineering. To predict coal bump disaster accurately and reliably, we propose a depth neural network (DNN) prediction model based on the dropout method and improved Adam algorithm. The coal bump accident examples were counted in order to analyze the influencing factors, characteristics, and causes of this type of accidents. Finally, four indexes of maximum tangential stress of surrounding rock, uniaxial compressive strength of rock, uniaxial tensile strength of rock, and elastic energy of rock are selected to form the prediction index system of coal bump. Based on the research results of rock burst, 305 groups of rock burst engineering case data are collected as the sample data of coal bump prediction, and then, the prediction model based on a dropout and improved Adam-based deep neural network (DA-DNN) is established by using deep learning technology. The DA-DNN model avoids the problem of determining the index weight, is completely data-driven, reduces the influence of human factors, and can realize the learning of complex and subtle deep relationships in incomplete, imprecise, and noisy limited data sets. A coal mine in Shanxi Province is used to predict coal bump with the improved depth learning method. The prediction results verify the effectiveness and correctness of the DA-DNN coal bump prediction model. Finally, it is proved that the model can effectively provide a scientific basis for coal bump prediction of similar projects.http://dx.doi.org/10.1155/2021/7794753 |
| spellingShingle | Shuang Gong Yi Tan Wen Wang Prediction and Evaluation of Coal Mine Coal Bump Based on Improved Deep Neural Network Geofluids |
| title | Prediction and Evaluation of Coal Mine Coal Bump Based on Improved Deep Neural Network |
| title_full | Prediction and Evaluation of Coal Mine Coal Bump Based on Improved Deep Neural Network |
| title_fullStr | Prediction and Evaluation of Coal Mine Coal Bump Based on Improved Deep Neural Network |
| title_full_unstemmed | Prediction and Evaluation of Coal Mine Coal Bump Based on Improved Deep Neural Network |
| title_short | Prediction and Evaluation of Coal Mine Coal Bump Based on Improved Deep Neural Network |
| title_sort | prediction and evaluation of coal mine coal bump based on improved deep neural network |
| url | http://dx.doi.org/10.1155/2021/7794753 |
| work_keys_str_mv | AT shuanggong predictionandevaluationofcoalminecoalbumpbasedonimproveddeepneuralnetwork AT yitan predictionandevaluationofcoalminecoalbumpbasedonimproveddeepneuralnetwork AT wenwang predictionandevaluationofcoalminecoalbumpbasedonimproveddeepneuralnetwork |