Research on the Processing of Coal Mine Water Source Data by Optimizing BP Neural Network Algorithm With Sparrow Search Algorithm

Abstract

Coal mine safety is crucial to the healthy and sustainable development of the coal industry, and coal mine flood is a major hidden danger of coal mine accidents. Therefore, the processing of coal mine water source data 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 with the assistance of laser. The laser emission power was set to 100 mw, and the 405 nm laser was emitted to the measured water body to obtain 210 groups of fluorescence spectral data of experimental water samples. The standard normal variable transformation (SNV) and multiple scattering correction (MSC) of the pretreatment algorithm are used to denoise the data and improve the spectral specificity. Due to the excessive calculation of the initial data, principal component analysis (PCA) was used to model and reduce the dimension of seven water samples, so as to obtain small data and maintain the data characteristics of the original information. In order to identify the water inrush type of coal mine water source, the sparrow search algorithm (SSA) is used to optimize the BP neural network in this study. This is because the SSA algorithm has the advantages of strong optimization ability and fast convergence rate compared with particle swarm optimization(PSO) and other optimization algorithms. Experiments show that under the premise of SNV pretreatment, the R 2 of SSA-BP model is infinitely close to 1, MRE is 0.0017, RMSE is 0.0001, the R 2 of PSO-BP model is 0.9995, MRE is 0.0026, RMSE is 0.0019, the R 2 of BP model is 0.9983, MRE is 0.0140, RMSE is 0.0075. Therefore, SSA-BP model is more suitable for the classification of coal mine water sources.

A Robustness Temperature Inversion Method for Cable Straight Joints Based on Improved Sparrow Search Algorithm Optimized BPNN

“…Due to the high sensitivity of BPNN to weight and threshold values [41]. In the continuous iteration, if the return error is large, the autonomous learning time consuming will be greatly increased.…”

Identification of Mine Mixed Water Inrush Source Based on Genetic Algorithm and XGBoost Algorithm: A Case Study of Huangyuchuan Mine

“…However, hydrochemical methods require sampling experiments to determine the various concentrations of ions and molecules in the solution and eliminate any interference that could affect the rapid identification of water sources [33]. At the same time, many water inrush disasters have shown that the water inrush mode and the inrush points may involve various water sources [34].…”

Study on key technology of identification of mine water inrush source by PSO-LightGBM

“…Traditional methods of water temperature and water level discrimination or direct analysis the hydrochemical data are being replaced by semi-quantitative analysis methods. Most widely used methods are combined with mathematical theory analysis, such as the cluster analysis method (Panagopoulos et al 2016;Zhang et al 2019), fuzzy mathematics method (Tiantian et al 2019), grey theory method (Ju et al 2019), bayesian discriminant method (Bogardi et al 1982;Wu et al 2016), fisher feature extraction algorithm (Wang et al 2021), GIS theoretical method (Donglin et al 2012), SVM algorithm (Ma et al 2018), and neural network algorithm (Chen et al 2021;Yan et al 2021). The above identification models are generally fast and effective, and machine learning methods have better applicability and advantages in identifying water inrush sources.…”