# Coal Mine Water Inflow Prediction Model Based on Multi-Factor Pearson Correlation Analysis
## 5 Conclusions  
A coal mine water inflow prediction model based on multi-factor Pearson correlation analysis is developed, where a convolutional neural network and bidirectional long short-term memory neural network are adopted to extract features from time-series data. To validate the performance of the present prediction model, a case study is conducted. Compared to other prediction models, the present prediction model can predict the magnitude and development trends of coal mine water inflow in the next 8 h more accurately, where the mean absolute percentage error is 5.76% and the correlation coefficient is 0.922, improving coal mining safety management. Meanwhile, an ablation experiment is conducted, where the effect of the bidirectional long short-term memory network, attention mechanism, and Pearson correlation analysis is investigated, demonstrating the advantage of the present prediction model over other prediction models.  
Of course, there are some limitations of the present study. Firstly, the present prediction model can only predict overall coal mine water inflow, not local coal mine water inflow. Secondly, based on the collected multi-source dataset, coal mine water inflow in the next 8 h is predicted. If we want to predict water inflow for further hours, the accuracy of predicting water inflow will decrease, since the collected dataset is insufficient. In the future, we will  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MAE** & **MAPE** & **RMSE** & **R2** \\ \hline Present & 40.707 & 5.764\% & 57.606 & 0.922 \\ XGBoost & 52.932 & 7.427\% & 69.628 & 0.886 \\ RF & 56.327 & 7.922\% & 73.505 & 0.873 \\ KNN & 63.499 & 9.090\% & 82.756 & 0.840 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative experiment between the present prediction model and other prediction models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MAE** & **MAPE** & **RMSE** & **R2** \\ \hline Present & 40.707 & 5.764\% & 57.606 & 0.922 \\ CNN-LSTM & 63.548 & 8.755\% & 82.616 & 0.840 \\ CNN-BiLSTM & 47.430 & 6.711\% & 63.705 & 0.905 \\ CNN-LSTM-Attention & 44.881 & 6.274\% & 61.765 & 0.910 \\ CNN-LSTM-Pearson & 59.713 & 8.268\% & 77.624 & 0.859 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiment between the present prediction model and other prediction models.  
optimize various parameter configurations of the present prediction model, and construct a coal mine water inflow risk warning framework to adapt to water inflow prediction and warnings under actual working conditions.  
Conceptualization, J.H.; Methodology, T.F.; Software, H.Y.; Validation, L.A.; Formal analysis, Z.L.; Resources, W.C.; Writing--original draft, L.M. All authors have read and agreed to the published version of the manuscript.  
The present study is supported by the National Natural Science Foundation of China (42374176), the National Key R&D Program of China (2022 YFC3005900), the Open Fund Project of Shaanxi Provincial Key Laboratory of Coal Mine Water Hazard Prevention and Control Technology (2021 SKMS07), Fundamental Research Funds for the Central Universities, China University of Geosciences (CUG2642022006) and Open Fund Project of State Key Laboratory of Gas Disaster Detecting, Preventing and Emergency Controlling (2022 SKLKF08).  
Data will be made available on reasonable request.  
Authors [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company China Coal Science and Industry Xi'an Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.