An evaluation of the mine water inrush based on the deep learning of ISMOTE

Abstract

In order to establish an effective coal mine floor water inrush prediction model, a neural network prediction method of water inrush based on an improved SMOTE algorithm expanding a small sample dataset and optimizing a deep confidence network was proposed. ISMOTE is used to enlarge the coal mine's measured data collection, while PCA is used to minimize the data's dimension. DBN is used to extract water inrush data features and estimate water inrush danger in coal mines. As the water inrush samples are small, they cannot provide enough information about the occurrence of water inrush accidents, which affects the DBN analysis of water inrush accidents, reduces the prediction accuracy, and causes safety hazards when mining in the coal mines. An improved SMOTE algorithm is used to expand the dataset. The DBN network is used to extract the secondary features of the nonlinear data after processing. Finally, a prediction model is established to predict coal mine water inrush. The superiority of this method is verified by the comparison between the actual condition and the prediction of the measured working face in a typical mining area in North China. The prediction accuracy of coal mine water inrush obtained by the model proposed in this paper is 94%, while the prediction accuracy of traditional BP algorithm is 70%, and the prediction accuracy of SAE algorithm is 91%, better than the rates of other methods. The findings of this study can be used to better predict and analyze coal mine water inrush accidents, improve the accuracy of water inrush accident prediction, and encourage the use of deep learning in coal mine floor water inrush prediction, all of which have theoretical and practical implications.

Emission prediction of oil-type gas out of the coal seam floor based on integration of advanced electrical resistivity exploration techniques with the GA-BP

“…In recent years, there has been a significant surge of interest in utilizing deep learning techniques to address complex challenges posed by collective effects. Numerous successful applications in geological disaster prediction, such as landslides (Zhang et al, 2019;Zhou et al, 2016), debris flow (Kern et al, 2017), and water inrush (Hu et al, 2013b;Dong et al, 2019;Shams et al, 2023;Ye et al, 2023), have demonstrated the efficacy of employing deep learning in scenarios involving similar mechanisms. To enhance the precision of evaluating oil-type gas emission disasters, a training model is proposed that combines Genetic Algorithm (GA) with Backpropagation (BP) neural network techniques (Zhu et al, 2023).…”

A multi-constraint and multi-objective optimization layout method for a mine water inrush monitoring network

“…Zhang et al developed an integrated floor water inrush model to predict the risk of water inrush caused by highly confined aquifers under complex stress conditions 11 . Ye et al proposed a neural network prediction method based on deep learning and used the improved synthetic minority oversampling technique (ISMOTE) to improve the accuracy of water inrush predictions 12 . Studies of the prediction and risk assessment theory for mine water hazards have led to a decreasing trend of water inrush accidents over time 13 .…”