Affiliation:
1. School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China
2. National Investment Hami Energy Development Co., Ltd., Hami 839000, China
Abstract
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 × 10−4, 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.
Funder
National Natural Science Foundation of China
China Coal’s “Enlisting and Leading” project
Subject
Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry
Reference34 articles.
1. Song, C.Y. (2017). Study and Application of Meso-Structural Characteristics and Deformation and Failure Mechanism of Weakly Cemented Sandstone, University of Science and Technology Beijing.
2. Influence factors of driving disturbance of adjacent roadway under the condition of weakly cemented surrounding rock;Song;J. Min. Saf. Eng.,2016
3. Physical and mechanical properties of weakly cemented strata in typical western mining areas;Sun;J. Coal,2019
4. Water disaster prevention and control technology and application of typical roof of Jurassic coalfield in Ordos Basin;Dong;J. Coal,2020
5. Further discussion on the “Three-map-double prediction method” for predicting and evaluating the risk of water inrush from coal seam roof;Wu;J. Coal,2016