# An Evaluation of Mine Water Inrush Based on Data Expansion and Machine Learning
## 4 Conclusions  
To solve the problems of small numbers of floor water inrush data samples and difficulty in extracting water inrush features, the _NaN-ISMOTE_ and _MISAE_ methods were introduced to improve the prediction model. The NaN-ISMOTE algorithm expands the water inrush dataset while addressing the issue of noise and preventing changes to the dataset's distribution pattern. This paper introduces the natural neighbor theory to reduce the risk of overfitting of the prediction model by removing outliers and noise samples from the water inrush dataset. The water inrush data samples are divided into center minority class samples and borderline minority class samples, and two targeted oversampling methods are proposed for oversampling. These two methods improve the diversity of the synthetic data and enhance the coverage of the minority class data area by synthetic samples while retaining the distribution pattern of the dataset and reducing the impact of borderline samples on oversampling. The MISAE is used to extract water inrush accident features, and mutual information is used to evaluate the importance of neurons in the \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **NaN-ISMOTE-MISAE** & **IWOA-SVM** & **PCA-OPF** & **PCA-Fisher** \\ \hline _Accuracy_ & **0.9375** & 0.9000 & 0.8625 & 0.8625 \\ _Recall_ & **0.9444** & 0.9412 & 0.9200 & 0.9375 \\ _F1 Score_ & **0.9533** & 0.9231 & 0.8932 & 0.8911 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The simulation results. Figure 8: Simulation comparison between _NaN-ISMOTE-MISAE_ and water inrush prediction algorithms. hidden layer of the model for predicting the target. Suppressing the interference of factors that are less relevant to the occurrence of water inrush accidents in the model improves the feature extraction performance of the classifier and increases the model prediction accuracy. The experimental results show that the model proposed in this paper can more accurately analyze the characteristics of water inrush accident, improve the accuracy of water inrush accident prediction, and promote the application of deep learning and data extension in coal mine water inrush prediction. The proposed ISMOTE method also has good performance in public datasets. Building upon foundational research in the field, this study incorporates machine learning theory and data augmentation techniques to conduct systematic investigations, yielding promising preliminary results. Subsequent research work can be conducted on the following aspects:  
1. The pseudo-label approach still has the flaw of having a fuzzy boundary between positive and negative data, as well as noise in the expansion of water-inrushing data. While this problem is somewhat resolved by the improved approach shown in this paper, it remains unresolved. In the subsequent research, the field of floor water inrush may be subjected to novel data-generating methods, such as GAN networks, and the data may undergo preprocessing and subsequent prediction. 2. The majority of current data-based modeling techniques focus on static data and ignore the water inrush accident's dynamic process. To improve the integrity and diversity of the floor water inrush dataset, future work must take into account the time dynamics of the data and collaborate with coal mines to collect time period series data of mine water inrush control factors during data collection. 3. The control factors of water inrush in mine floor will change with the process of coal mining. The existing algorithms mainly focus on the study of constant weight statistics, and the model parameters can be updated in real time in future work through the variable weight mechanism. All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [PERSON] and [PERSON]. The manuscript was written by [PERSON] and all authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.