# Coal Mine Water Inflow Prediction Model Based on Multi-Factor Pearson Correlation Analysis
## 3 Correlation Analysis of Water Inflow  
To conduct the present study, water inflow data from Jianxin coal mine, Shaanxi Province, China, is collected to predict coal mine water inflow. Firstly, the mining depth of Jianxin coal mine is close to the aquifer thickness, and the risk of coal mine water inflow is relatively high, making it necessary to predict coal mine water inflow. Secondly, Jianxin coal mine is equipped with a smart coal mine data collection system, making it convenient to collect multi-source data to predict coal mine water inflow, including directly related water inflow data from different zones and indirectly related water inflow data from different environmental factors. The sampling time was from 0:00 on 1 September 2024 to 24:00 on 9 September 2024, and the sampling interval was 3.8 min. Figure 5 shows directly related water inflow data from different zones, and Figure 6 shows indirectly related water inflow data from different environmental factors, like aquifer thickness, aquitard thickness, atmospheric temperature, development extension length, goat area, layer thickness, mining depth, minute coal mining volume, and rainfall. After multi-source water inflow data was collected, we conducted Pearson correlation analysis [25; 26] to select factors with strong correlations with water inflow to construct the training dataset.  
Figure 4: CNN-BiLSTM-Attention model.  
To represent the correlation between environmental factors and water inflow, the Pearson correlation coefficient matrix is calculated. The correlation is divided by 0.2; indicators with a correlation lower than 0.2 are considered unrelated to water inflow. By conducting Pearson correlation analysis, the correlation between environmental factors and water inflow is relatively high, including aquifer thickness, coal seam thickness, mining depth, minute coal mining volume, rainfall, river water level elevation, and temperature, i.e., these environmental factors affect water inflow significantly, so they are selected to construct the dataset to train the water inflow prediction model. The correlation between environmental factors and water inflow is relatively low, including aquitard thickness, development extension length, and goat area; to achieve higher computational speed and ensure prediction accuracy, they are removed from the training dataset. Based on the Pearson correlation analysis, a multi-factor water inflow prediction dataset is constructed. The dataset consists of three parts: the first part is timestamps, the second part is environmental factors, and the third part is water inflow. The dataset related to water inflow includes directly related data and indirectly related data, like aquifier thickness, coal seam thickness, mining depth, minute coal mining volume, rainfall, river water level elevation, and temperature; 3410 pieces of data related to water inflow are collected for water inflow prediction model training.  
Figure 5: Directly related water inflow data from different zones.  
Figure 6: Indirectly related water inflow data from different environmental factors.