Prediction of the First Weighting from the Working Face Roof in a Coal Mine Based on a GA-BP Neural Network

The accidents caused by roof pressure seriously restrict the improvement of mines and threaten production safety. At present, most coal mine pressure forecasting methods still rely on expert experience and engineering analogies. Artificial neural network prediction technology has been widely used in coal mines. This new approach can predict the surface pressure on the roof, which is of great significance in coal mine production safety. In this paper, the mining pressure mechanism of coal seam roofs is summarized and studied, and 60 sets of initial pressure data from multiple working surfaces in the Datong mining area are collected for gray correlation analysis. Finally, 12 parameters are selected as the input parameters of the model. Suitable back propagation (BP) and GA(genetic algorithm)-BP initial roof pressure prediction models are established for the Datong mining area and trained with MATLAB programming. By comparing the training results, we found that the optimized GA-BP model has a larger determination coefficient, smaller error, and greater stability. The research shows that the prediction method based on the GA-BP neural network model is relatively reliable and has broad engineering application prospects as an auxiliary decision-making tool for coal mine production safety.

[1]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[2]  Shervin Motamedi,et al.  Soft computing approaches for forecasting reference evapotranspiration , 2015, Comput. Electron. Agric..

[3]  Yuping Wang,et al.  An orthogonal genetic algorithm with quantization for global numerical optimization , 2001, IEEE Trans. Evol. Comput..

[4]  Masoud Monjezi,et al.  Backbreak prediction in the Chadormalu iron mine using artificial neural network , 2012, Neural Computing and Applications.

[5]  Manoj Khandelwal,et al.  Application of soft computing to predict blast-induced ground vibration , 2011, Engineering with Computers.

[6]  Ian Lowndes,et al.  The application of a coupled artificial neural network and fault tree analysis model to predict coal and gas outbursts , 2010 .

[7]  Pejman Tahmasebi,et al.  A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation , 2012, Comput. Geosci..

[8]  Elif Bahadir.  Prediction of Prospective Mathematics Teachers' Academic Success in Entering Graduate Education by Using Back-Propagation Neural Network. , 2016 .

[9]  Chunmei Liu.  Price Forecast for Gold Futures Based on GA-BP Neural Network , 2009, 2009 International Conference on Management and Service Science.

[10]  Tomaž Ambrožič,et al.  Prediction of subsidence due to underground mining by artificial neural networks , 2003 .

[11]  Shinji Takeuchi,et al.  Grey model for prediction of pore pressure change , 2010 .

[12]  Babak Nadjar Araabi,et al.  A model for determining the cyclic swell–shrink behavior of argillaceous rock , 2008 .

[13]  Jie Song,et al.  Human attitudes in environmental management: Fuzzy Cognitive Maps and policy option simulations analysis for a coal-mine ecosystem in China. , 2013, Journal of environmental management.

[14]  Radoslaw Winiczenko,et al.  Optimisation of ANN topology for predicting the rehydrated apple cubes colour change using RSM and GA , 2016, Neural Computing and Applications.

[15]  Jixiong Zhang,et al.  The Influences of Key Strata Compound Breakage on the Overlying Strata Movement and Strata Pressure Behavior in Fully Mechanized Caving Mining of Shallow and Extremely Thick Seams: A Case Study , 2019, Advances in Civil Engineering.

[16]  Rahman Ashena,et al.  Bottom hole pressure estimation using evolved neural networks by real coded ant colony optimization and genetic algorithm , 2011 .

[17]  Yong He,et al.  [Discrimination of varieties of apple using near infrared spectra based on principal component analysis and artificial neural network model]. , 2006, Guang pu xue yu guang pu fen xi = Guang pu.

[18]  Kees Wevers,et al.  Grey System Theory and Applications: A Way Forward , 2007 .

[19]  T. N. Singh,et al.  Prediction of blast-induced ground vibration using artificial neural network , 2009 .

[20]  Z.A. Bashir,et al.  Applying Wavelets to Short-Term Load Forecasting Using PSO-Based Neural Networks , 2009, IEEE Transactions on Power Systems.

[21]  S. H. Cao,et al.  Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis , 2006 .

[22]  Guang-Qiao Lin,et al.  The Measurement of Underground Pressure Regulation in Ulan Moron Coal Mine , 2014 .

[23]  Siamak Haji Yakhchali,et al.  A novel fuzzy inference system for predicting roof fall rate in underground coal mines , 2013 .

[24]  Liu Ke,et al.  Research on the Forecast Model of Electricity Power Industry Loan Based on GA-BP Neural Network , 2012 .

[25]  Jhareswar Maiti,et al.  Development of a relative risk model for roof and side fall fatal accidents in underground coal mines in India. , 2009 .

[26]  S. K. Palei,et al.  Logistic regression model for prediction of roof fall risks in bord and pillar workings in coal mines: an approach. , 2009 .

[27]  H. H. Einstein,et al.  Assessment and management of roof fall risks in underground coal mines , 2004 .