Identification of Mine Water Inrush Source Based on PCA-BP Neural Network

It is of great significance to analyze the chemical indexes of mine water and develop a rapid identification system of water source, which can quickly and accurately distinguish the causes of water inrush and identify the source of water inrush, so as to reduce casualties and economic losses and prevent and control water inrush disasters. Taking Ca 2+ , Mg 2+ ,


Introduction
In June 2021, the Coal Industry Association issued "14 th Five-Year" high-quality development guidance for coal industry' [1], pointing out that by the end of the "14 th Five-Year Plan", China's energy consumption is still dominated by coal.
Therefore, in order to ensure the long-term and stable development of China's economy, it is necessary to mine coal resources safely and reasonably to ensure the stable production of coal. Water inrush accident is the second largest disaster in coal mine production, but its economic loss is in the first place, and it is also ures to prevent and control water, which can greatly reduce the occurrence of water inrush accidents.

Overview of Study Area
In this paper, the central area of Panxie mining area in Huainan coalfield, located in the southern margin of Huaibei Plain, is taken as the research area. The area is dominated by Huaihe River, surrounded by small rivers such as Xifei River, Jiahe River and Nihe River, as well as two large lakes such as Huajia Lake and Jiaogang Lake and several small lakes such as coal mining subsidence water area. The hydrogeological conditions are mainly controlled by neotectonic movement and regional structure, and the groundwater in deep and shallow parts is obviously different. The main aquifers in the study area are composed of Cenozoic loose layer pore aquifer (group), Permian sandstone fissure aquifer (group), Carboniferous Taiyuan Formation limestone fissure karst aquifer, Ordovician limestone fissure karst aquifer and so on. The division of the aquifer is shown in Figure 1.

PCA
PCA is a multivariate statistical method using the idea of "linear dimensionality reduction", which is used to characterize more original indicators with a few comprehensive indicators through a certain linear projection [4] [5] [6]. While retaining the vast majority of information in the original indicators, the correlation between the indicators is eliminated. The comprehensive indicators obtained by this method are called the main components [7] [8]. In specific problems, high-dimensional data is not conducive to training better parameters because of its large dispersion, while low-dimensional data can better train parameters. Therefore, through the form of dimensionality reduction, high-dimensional data indicators are mapped to low-dimensional space, and then the data with the

Hydrochemical Characteristics Analysis
Because the chemical composition of groundwater in different aquifers is different, the difference of hydrochemical characteristics can be used to judge the type of mine water inrush source. The concentrations of Ca 2+ , Mg 2+ , K + , Na + , CO − and Cl − are often selected as the discriminant indexes in the hydrochemical method of mine water inrush source identification. In this paper, the hydrogeological and water inrush data related to Panxie Mine are analyzed [11], and it is judged that the hydrogeological type of the International Journal of Geosciences mining area is medium, and the mine has repeatedly occurred water inrush accidents. A total of 96 sets of water inrush data of Guqiao Mine, Dingji Mine and Pansan Mine were collected as research samples, it includes three prediction types: Cenozoic lower water (expressed by I), coal measure water (expressed by II) and Taiyuan Formation limestone water (expressed by III). A part of water inrush data in the study area is shown in Table 1.
The water chemical composition of 96 groups of data in Table 1 was statistically analyzed, and the results are shown in Table 2. The groundwater in the mining area is alkaline. From the mean value of TDS, it is judged that the groundwater in the mining area is medium mineralized water, and the TDS value of water samples in the study area is very different. On the whole, the mass concentration of the main components of cations in groundwater in the mining area is Na + + K + > Ca 2+ > Mg 2+ , The concentration of anion, the main component of anion in Cenozoic lower water and Taiyuan Formation limestone water  Compared with lower aquifer and coal measure water, the ion dispersion in limestone water of Taiyuan Formation is poor and the uniformity is low, which may be disturbed by external aquifer.  Table 3.

Principal Component Analysis of Original Data Based on SPSS
It can be seen from Table 3 that the correlation coefficients of Na + + K + and Using SPSS to reduce the dimension of the data, the total variance in Table 4 is first obtained. According to the cumulative variance contribution rate, four

Water Inrush Source Identification Based on PCA-BP Neural Network
In this study, there were 96 sets of data, 76 sets of data as training samples, and 20 sets of data as prediction samples. Before the beginning of BP neural network training, in order to eliminate the adverse effects caused by singular sample data, it is necessary to normalize the samples and limit the data to the range of [0,1] or [−1, 1]. The normalized data can accelerate the speed of gradient descent to find the optimal solution, improve the training accuracy of the network, and avoid the numerical complexity in the calculation process. The normalization results of the sample data are shown in Table 5.  and the network training error is 0.036408, the accuracy reaches the training standard. As shown in Figure 2, it can be found that in the fitting data completed by BP neural network training, the R value of the training set is 0.82478, the R value of the verification set is 0.91443, and the R value of the test set is 0.40698. After the training, the dynamic system simulation function predict = sim (net, C) is called in the editor to get the simulation prediction results(C is the forecast data). Comparing the simulation results with the actual results, the No.64 coal measure water in Pansan Mine was misjudged as limestone water.
The overall number of misjudged samples was 1, and the accuracy of water source identification was 95%.

Conclusion
The principal component analysis of 96 groups of data was carried out by SPSS25.0 software to determine the principal component score coefficient, and four new discriminant indexes were obtained to replace the original hydrochemical indexes. BP neural network training and prediction is carried out by Matlab R2021b software, in which the number of misjudged samples is 1, the accuracy of water source identification is 95%, and the effect of water inrush source identification is very good. It can accurately and quickly identify the water source of mine water inrush and realize the early warning analysis of water inrush accident.
However, the established PCA-BP neural network model only considers the hydrochemical characteristics. In the subsequent research, the factors affecting water level, water temperature and water inflow can be comprehensively considered to International Journal of Geosciences improve the water source determination model and improve the discrimination accuracy.