# Coal Mine Water Inflow Prediction Model Based on Multi-Factor Pearson Correlation Analysis
## 1 Introduction  
The coal mine water inflow is the key indicator for evaluating coal mine complexity, affecting the economic evaluation of the coal mine. Additionally, the coal mine water inflow is the important basis of developing water prevention and control projects. Therefore, to ensure coal mining safety, predicting coal mine water inflow is necessary. Currently, there are two prediction methods of coal mine water inflow: one is the traditional prediction method based on the stable flow theory, and the other is the modern prediction method based on deep learning, where the magnitude and development trends of coal mine water inflow are predicted [1, 2, 3, 4, 5, 6, 7, 8]. [PERSON] et al. [1] developed a coal mine water inflow risk assessment and decision support system based on a Bayesian network. [PERSON] et al. [2] established a water inflow prediction model with geological, hydrogeological, and coal mine conditions. [PERSON] et al. [3] addressed the limitations of using the water inflow coefficient method to evaluate coal mine water inflow risk, and a comprehensive analysis was conducted. [PERSON] et al. [4] considered the geological and hydrogeological data of a coal mine and calculated the weights of indicators based on the attribute hierarchy model. Furthermore, some scholars trained models based on data-driven approaches to predict water inflow. [PERSON] et al. [5] extracted hydrochemical discrimination ions from water samples and proposed an improved water source discrimination model that combined data mining, classification models, and learning reinforcement algorithms. [PERSON] et al. [6] proposed a data-driven method to predict coal mine water inflow using on-site 3D microseismic monitoring data and demonstrated the effectiveness of the method. [PERSON] et al. [7] established a water inflow prediction modelby predicting the water inflow rate. [PERSON] et al. [8] proposed a prediction model that utilized microseismic energy density analysis to monitor fracture points within rock layers, providing real-time predictions. With the development of machine learning algorithms, the long short-term memory (LSTM) network and its improved algorithms achieve good results in time-series prediction in many fields [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. [PERSON] et al. [9] developed a two-layer LSTM model to predict the depth of the groundwater level, using multiple inputs to predict changes in the groundwater level. [PERSON] et al. [15] used an LSTM network to fill in missing data of groundwater levels, and retrieved the missing data without the need for calibration. [PERSON] et al. [16] applied deep learning to the analysis and prediction of coal mine water inflow, which improved the accuracy of water inflow prediction. [PERSON] et al. [17] used an LSTM network to predict the time series of key water quality indicators, which achieved accurate prediction results. [PERSON] et al. [19] proposed the DIFF-LSTM model, which effectively stabilized non-stationary water inflow sequences and reduced autocorrelation of the sequences. To predict coal mine water inflow, [PERSON] et al. [20] added random search and Bayesian optimization algorithms to the LSTM model. The rapid development of machine learning-based methods enables the relatively accurate prediction of coal mine water inflow. However, the above studies are limited to time-series analyses, without considering the effect of multiple factors on water inflow, including aquifer thickness, coal mining volume per minute, coal seam thickness, goat area, mining depth, and rainfall. Obviously, coal mine water inflow is affected by the above factors. To improve the accuracy of predicting coal mine water inflow, it is necessary to study the correlation between the above factors and water inflow.