An Evaluation of Mine Water Inrush Based on Data Expansion and Machine Learning

Abstract

The accuracy of coal mine water inrush prediction models is affected mainly by the small number of samples and difficulty in feature extraction. A new data augmentation water inrush prediction method is proposed. This method uses the natural neighbor theory and mutual information sparse autoencoder-improved SMOTE to augment and predict the risk of water inrush. By learning features through the autoencoder, we can achieve better separation between classes and weaken the influence of data overlap between classes in the original sample. Then, the natural neighbor search algorithm is used to determine the intrinsic neighbor relationships between samples, remove outliers and noise samples, and use different oversampling methods for borderline samples and center samples in the minority class. Synthetic samples are generated in the feature space, mapped back to the original space, and merged with the original samples to form an expanded water inrush dataset. Finally, the experiment demonstrates that the enhanced SMOTE oversampling algorithm suggested in this paper broadens the dataset. With a Gmean value of 0.9025 from training with the standard dataset, it outperforms the contrast algorithm, SMOTE average of 0.8581, B-SMOTE average of 0.873, and ADASYN average of 0.8909. Additionally, it performs well in the coal mine floor water inrush dataset, increasing the water inrush prediction algorithm’s accuracy.

Keywords

• mine water inrush
• feature extraction
• natural neighbor theory
• improved SMOTE
• mutual information dropout
• sparse autoencoder