# An Evaluation of Mine Water Inrush Based on Data Expansion and Machine Learning
## 3 Results and Discussions
### NaN-ISMOTE-MISAE Algorithm Ablation Experiment  
The _NaN-ISMOTE-MISAE_ algorithm proposed in this article first uses natural neighbors to remove noise points and outliers and then uses different interpolation methods for data expansion according to the different areas where the minority class samples are located. Second, the prediction model uses the _MISAE_ algorithm to extract highly relevant water inrush features. Therefore, two SMOTE prediction models without natural neighbor data preprocessing and refined sampling methods were used to verify the effectiveness of the proposed oversampling method. The prediction results of the _SAE_ and _MISAE_ were compared using the same dataset to verify the effectiveness of the proposed water inrush feature extraction method. Figure 7 and Table 3 shows the prediction curves of the different models. Figure 7: _Cont_. The comparison of Figure 7 and Table 3 with the comparison algorithm demonstrates that the best results were obtained with the same dataset and operating environment in terms of prediction accuracy, recall rate, and F1 Score. This study compared the prediction accuracy while calculating the recall rate and F1 Score due to the small sample size of the water inrush samples in the dataset. The algorithm suggested in this research has the highest recall rate and F1 score, as the table illustrates. It demonstrates that the _NaN-ISMOTE-MISAE_ can successfully recognize small samples of water inrush data and has strong sensitivity. It is evident from a comparison of the _ISMOTE-SAE_ and _ISMOTE-MISAE_ algorithm results in the table that the _SAE_ algorithm performs poorly in terms of sensitivity and small sample classification. This is because the _SAE_ algorithm treats input data equally when extracting features. However, the same weight will extract variable information with less correlation since different factors contribute to the occurrence of water inrush accidents in varying degrees, which will reduce the forecast accuracy. The _MISAE_ algorithm, on the other hand, increases the accuracy of feature extraction by keeping the neurons that are favorable to the expression of water inrush characteristics and resets the weight of the less relevant feature neurons to 0. For small-sample water inrush data, _ISMOTE-MISAE_ \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**NaN-ISMOTE-MISAE**} & \multicolumn{1}{c}{**NaN-ISMOTE-SAE**} & \multicolumn{1}{c}{**BLSMOTE-MISAE**} & \multicolumn{1}{c}{**SMOTE-MISAE**} \\ \hline _Accuracy_ & **0.9375** & 0.9125 & 0.9 & 0.8750 \\ _Recall_ & **0.9524** & 0.9259 & 0.9245 & 0.9057 \\ _F1 Score_ & **0.9362** & 0.9346 & 0.9245 & 0.9057 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation experiment results. Figure 7: Ablation experiment results. performs better in terms of accuracy and prediction outcomes when compared to the _ISMOTE-SAE_ algorithm under the same data expansion settings. The model proposed in this paper performs better under the same prediction algorithm when compared to the prediction results of _ISMOTE-MISAE_, _SMOTE-MISAE_, and _BLSMOTE-MISAE_. This is because the natural neighbor technology is introduced to preprocess the dataset, remove noise points and outliers, and create a dataset with a regular distribution. The prediction model's capacity for generalization is enhanced. In the safe sample zone and the edge sample, the _ISMOTE_ algorithm uses distinct sampling techniques. When edge samples are oversampled, the likelihood of producing noise is effectively decreased by randomly interpolating the nearest natural intermediate points. The diversity of the synthetic dataset and the precision of the ensuing prediction algorithm can both be increased by using the spatial random interpolation approach in the safe sample area. As illustrated in the accompanying figures and tables, the _NaN-ISMOTE-MISAE_ framework incorporates an anomaly detection mechanism grounded in natural neighbor theory to systematically eliminate outliers.