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.
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