A Prediction Model of Coal Seam Roof Water Abundance Based on PSO-GA-BP Neural Network
With the gradual increase of coal production capacity, the issue of water hazards in coal seam roofs is increasing in prominence. Accurate and effective prediction of the water content of the roof aquifer, based on limited hydrogeological data, is critical to the identification of the central area o...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/23/4117 |
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| author | Xue Dai Xiaoqin Li Yuguang Zhang Wenping Li Xiangsheng Meng Liangning Li Yanbo Han |
| author_facet | Xue Dai Xiaoqin Li Yuguang Zhang Wenping Li Xiangsheng Meng Liangning Li Yanbo Han |
| author_sort | Xue Dai |
| collection | DOAJ |
| description | With the gradual increase of coal production capacity, the issue of water hazards in coal seam roofs is increasing in prominence. Accurate and effective prediction of the water content of the roof aquifer, based on limited hydrogeological data, is critical to the identification of the central area of prevention and control of coal seam roof water damage and the reduction of the incidence of such accidents in coal mines. In this paper, we establish a prediction model for the water abundance of the roof slab aquifer, using a PSO-GA-BP neural network. Our model is based on five key factors: aquifer thickness, permeability coefficient, core recovery, number of sandstone and mudstone interbedded layers, and fold fluctuation. The model integrates the genetic algorithm (GA) into the particle swarm optimization (PSO) algorithm, with the particle swarm optimization algorithm serving as the primary approach. It utilizes adaptive inertia weight and quadratic optimization of the weights and thresholds of the backpropagation neural network to minimize the output error threshold for the purpose of minimizing output errors. The prediction model is applied to hydrogeology and coal mine production for the first time. The model is trained using 100 data samples collected by the Surfer 13 software. These samples help to accurately predict the unit inflow of water. The model is then compared with traditional forecasting methods such as FAHP, BP, and GA-BP neural network models to determine its efficiency. The study found that the PSO-GA-BP neural network model accurately predicts aquifer water abundance with higher precision. The root mean square error (RMSE) of the test set is determined to be 8.7 × 10<sup>−4</sup>, and the fitting result is measured at 0.9999, indicating minimal error with actual values of the sample. According to the prediction results of the test set, the water abundance capacity of the No. 7 coal mine in Hami Danan Lake is divided, and it is found that the overall difference between the results and the actual value is small, which verifies the reliability of the model. According to the results of the water abundance division, strong water abundance areas are mainly concentrated in the third-partition area. This study provides a new method for the prediction of aquifer water abundance, improves the prediction accuracy of aquifer water abundance, reduces the cost of coal mine production, and provides a scientific evaluation method and a theoretical basis for the prevention and control of water disasters in coal seam roofs. |
| first_indexed | 2024-03-09T01:39:50Z |
| format | Article |
| id | doaj.art-2734bba2500d4281a1e923230387daaf |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T01:39:50Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-2734bba2500d4281a1e923230387daaf2023-12-08T15:28:32ZengMDPI AGWater2073-44412023-11-011523411710.3390/w15234117A Prediction Model of Coal Seam Roof Water Abundance Based on PSO-GA-BP Neural NetworkXue Dai0Xiaoqin Li1Yuguang Zhang2Wenping Li3Xiangsheng Meng4Liangning Li5Yanbo Han6School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, ChinaNational Investment Hami Energy Development Co., Ltd., Hami 839000, ChinaSchool of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, ChinaWith the gradual increase of coal production capacity, the issue of water hazards in coal seam roofs is increasing in prominence. Accurate and effective prediction of the water content of the roof aquifer, based on limited hydrogeological data, is critical to the identification of the central area of prevention and control of coal seam roof water damage and the reduction of the incidence of such accidents in coal mines. In this paper, we establish a prediction model for the water abundance of the roof slab aquifer, using a PSO-GA-BP neural network. Our model is based on five key factors: aquifer thickness, permeability coefficient, core recovery, number of sandstone and mudstone interbedded layers, and fold fluctuation. The model integrates the genetic algorithm (GA) into the particle swarm optimization (PSO) algorithm, with the particle swarm optimization algorithm serving as the primary approach. It utilizes adaptive inertia weight and quadratic optimization of the weights and thresholds of the backpropagation neural network to minimize the output error threshold for the purpose of minimizing output errors. The prediction model is applied to hydrogeology and coal mine production for the first time. The model is trained using 100 data samples collected by the Surfer 13 software. These samples help to accurately predict the unit inflow of water. The model is then compared with traditional forecasting methods such as FAHP, BP, and GA-BP neural network models to determine its efficiency. The study found that the PSO-GA-BP neural network model accurately predicts aquifer water abundance with higher precision. The root mean square error (RMSE) of the test set is determined to be 8.7 × 10<sup>−4</sup>, and the fitting result is measured at 0.9999, indicating minimal error with actual values of the sample. According to the prediction results of the test set, the water abundance capacity of the No. 7 coal mine in Hami Danan Lake is divided, and it is found that the overall difference between the results and the actual value is small, which verifies the reliability of the model. According to the results of the water abundance division, strong water abundance areas are mainly concentrated in the third-partition area. This study provides a new method for the prediction of aquifer water abundance, improves the prediction accuracy of aquifer water abundance, reduces the cost of coal mine production, and provides a scientific evaluation method and a theoretical basis for the prevention and control of water disasters in coal seam roofs.https://www.mdpi.com/2073-4441/15/23/4117water abundanceparticle swarm optimization algorithmgenetic algorithmBP neural networkFAHP |
| spellingShingle | Xue Dai Xiaoqin Li Yuguang Zhang Wenping Li Xiangsheng Meng Liangning Li Yanbo Han A Prediction Model of Coal Seam Roof Water Abundance Based on PSO-GA-BP Neural Network Water water abundance particle swarm optimization algorithm genetic algorithm BP neural network FAHP |
| title | A Prediction Model of Coal Seam Roof Water Abundance Based on PSO-GA-BP Neural Network |
| title_full | A Prediction Model of Coal Seam Roof Water Abundance Based on PSO-GA-BP Neural Network |
| title_fullStr | A Prediction Model of Coal Seam Roof Water Abundance Based on PSO-GA-BP Neural Network |
| title_full_unstemmed | A Prediction Model of Coal Seam Roof Water Abundance Based on PSO-GA-BP Neural Network |
| title_short | A Prediction Model of Coal Seam Roof Water Abundance Based on PSO-GA-BP Neural Network |
| title_sort | prediction model of coal seam roof water abundance based on pso ga bp neural network |
| topic | water abundance particle swarm optimization algorithm genetic algorithm BP neural network FAHP |
| url | https://www.mdpi.com/2073-4441/15/23/4117 |
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