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.
To provide an effective risk assessment of water inrush for coal mine safety production, a BP neural network prediction method for water inrush based on principal component analysis and deep confidence network optimization was proposed. Because deep belief network (DBN) is disadvantaged by a long training time when establishing a high-dimensional data classification model, the principal component analysis (PCA) method is used to reduce the dimensionality of many factors affecting the water inrush of the coal seam floor, thus reducing the number of variables of the research object, redundancy and the difficulty of feature extraction and shortening the training time of the model. Then, a DBN network was used to extract secondary features from the processed nonlinear data, and a more abstract high-level representation was formed by combining low-level features to find the expression of the nonlinear relationship between the characteristics of water in bursts. Finally, a prediction model was established to predict the water inrush in coal mines. The superiority of this method was verified by comparing the prediction of the actual working face with the actual situation in typical mining areas of North China. The prediction accuracy of coal mine water inrush obtained by this algorithm is 94%, while the prediction accuracy of traditional BP algorithm is 70%, and the prediction accuracy of SVM algorithm is 88%.
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.
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