Analysis of Potential Water Inflow Rates at an Underground Coal Mine Using a WOA-CNN-SVM Approach
The water yield of aquifers increases the risk of water inflow, threatens the safe production of coal mines, and even causes geological disasters and construction hazards. To predict water yield quickly and accurately, multiple composite factors are used to invert unit water inflow rates to judge wa...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/16/6/813 |
| _version_ | 1797239015519289344 |
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| author | Weitao Liu Yuying Ren Xiangxi Meng Bo Tian Xianghai Lv |
| author_facet | Weitao Liu Yuying Ren Xiangxi Meng Bo Tian Xianghai Lv |
| author_sort | Weitao Liu |
| collection | DOAJ |
| description | The water yield of aquifers increases the risk of water inflow, threatens the safe production of coal mines, and even causes geological disasters and construction hazards. To predict water yield quickly and accurately, multiple composite factors are used to invert unit water inflow rates to judge water yield grade. Taking the typical representative of north China-type coal fields as an example, six factors are selected: aquifer thickness, the radius of influence, normalized drawdown, permeability coefficient, the core rate of drilling holes, and the proportion of clay thickness to the thickness of the lower group. The whale optimization algorithm (WOA)–convolutional neural network (CNN)–support vector machine (SVM) model is established with the unit water inflow rate as the forecast target, and different models are selected for comparison. The water yield zoning map is obtained by bringing the borehole data into the model for prediction. The findings indicate that the root mean square error and average absolute error of the composite predictive model models are 0.0318 and 0.0268, respectively, and the model outperforms alternative models. The predicted water yield zoning aligns well with the actual conditions, offering a novel paradigm for water yield assessment. |
| first_indexed | 2024-04-24T17:44:49Z |
| format | Article |
| id | doaj.art-3944c19832ca4567a3392b4565bdeb5c |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-04-24T17:44:49Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-3944c19832ca4567a3392b4565bdeb5c2024-03-27T14:08:12ZengMDPI AGWater2073-44412024-03-0116681310.3390/w16060813Analysis of Potential Water Inflow Rates at an Underground Coal Mine Using a WOA-CNN-SVM ApproachWeitao Liu0Yuying Ren1Xiangxi Meng2Bo Tian3Xianghai Lv4College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaShandong Energy Group Co., Ltd., Jinan 250101, ChinaCollege of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaThe water yield of aquifers increases the risk of water inflow, threatens the safe production of coal mines, and even causes geological disasters and construction hazards. To predict water yield quickly and accurately, multiple composite factors are used to invert unit water inflow rates to judge water yield grade. Taking the typical representative of north China-type coal fields as an example, six factors are selected: aquifer thickness, the radius of influence, normalized drawdown, permeability coefficient, the core rate of drilling holes, and the proportion of clay thickness to the thickness of the lower group. The whale optimization algorithm (WOA)–convolutional neural network (CNN)–support vector machine (SVM) model is established with the unit water inflow rate as the forecast target, and different models are selected for comparison. The water yield zoning map is obtained by bringing the borehole data into the model for prediction. The findings indicate that the root mean square error and average absolute error of the composite predictive model models are 0.0318 and 0.0268, respectively, and the model outperforms alternative models. The predicted water yield zoning aligns well with the actual conditions, offering a novel paradigm for water yield assessment.https://www.mdpi.com/2073-4441/16/6/813risk analysisWOA-CNN-SVM modelwater yieldglobal search |
| spellingShingle | Weitao Liu Yuying Ren Xiangxi Meng Bo Tian Xianghai Lv Analysis of Potential Water Inflow Rates at an Underground Coal Mine Using a WOA-CNN-SVM Approach Water risk analysis WOA-CNN-SVM model water yield global search |
| title | Analysis of Potential Water Inflow Rates at an Underground Coal Mine Using a WOA-CNN-SVM Approach |
| title_full | Analysis of Potential Water Inflow Rates at an Underground Coal Mine Using a WOA-CNN-SVM Approach |
| title_fullStr | Analysis of Potential Water Inflow Rates at an Underground Coal Mine Using a WOA-CNN-SVM Approach |
| title_full_unstemmed | Analysis of Potential Water Inflow Rates at an Underground Coal Mine Using a WOA-CNN-SVM Approach |
| title_short | Analysis of Potential Water Inflow Rates at an Underground Coal Mine Using a WOA-CNN-SVM Approach |
| title_sort | analysis of potential water inflow rates at an underground coal mine using a woa cnn svm approach |
| topic | risk analysis WOA-CNN-SVM model water yield global search |
| url | https://www.mdpi.com/2073-4441/16/6/813 |
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