Classification of Coal Mine Water Sources by Improved BP Neural Network Algorithm
YAN Peng-cheng1, 2, SHANG Song-hang2*, ZHANG Chao-yin2, ZHANG Xiao-fei2
1. State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine, Anhui University of Science and Technology, Huainan 232001, China
2. College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
Abstract：Coal mine safety is very important to the healthy and sustainable development of the coal industry, and the coal mine flood is a major hidden danger of coal mine accidents. Therefore, coal mine water source data processing is of great significance to prevent mine water inrush accidents. In this experiment, the laser-induced fluorescence technology was used to obtain the data information of 7 water sources. The laser power was set at 100 mW, 405 nm laser was emitted to the measured water, and 210 groups of fluorescence spectrum data of experimental water samples were obtained. n order to eliminate the influence of fluorescence background, detector noise and power fluctuation, SG smoothing and multiplicative scatter correction (MSC) preprocessing is used to reduce the noise and improve the spectral specificity of the data. Due to a large amount of initial data operation, data compression, redundancy elimination and data noise elimination, principal components analysis (PCA) is used to analyze the seven water samples Row modeling and dimensionality reduction are used to obtain small data and keep the original data characteristics. In order to identify the water inrush type of coal mine water source, particle swarm optimization (PSO) is used to optimize BP neural network for dimension reduced data. PSO algorithm updates the position of individual extremum and population extremum by comparing the fitness value of new particle with that of individual extremum and population extremum, PSO algorithm updates the position of individual extremum and population extremum by comparing the fitness value of new particle with that of individual extremum and population extremum, and endows the optimal initial weight and threshold value to BP neural network, so as to predict and analyze the types of water samples to be measured. The common PSO optimized BP neural network is prone to premature convergence, so mutation factor is introduced into the improved PSO algorithm to improve the possibility of finding a better solution. Experimental results show that the SG algorithm performs well among SG, MSC, and original preprocessing methods and improves the correlation of models. On the premise of SG pretreatment, the determination coefficient R2 of BP is 0.984 5, the mean relative error MRE is 7.39%, and the root mean square error is 7.25%; the determination coefficient R2 of PSO-BP is 0.999 8, the mean relative error MRE is 0.17%, the root mean square error is 0.08%; the determination coefficient R2 of IPSO-BP is 0.999 9, the MRE and RMSE are 0.01%. The results show that the spectral data preprocessed by SG is more accurate than that by MSC, and the improved particle swarm optimization algorithm is more suitable for mine water source classification in this experiment.
