Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Abstract

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis

“…This is also attributed to the optimization of the two parameters, Mtry and Ntree, which improves the performance of the model. Meng et al (2021) established a coal mining face safety early warning model based on fuzzy C-means clustering and back propagation neural network, which could realize the prediction and early warning of the working face safety. The neural network model optimized by genetic algorithm had better performance than the traditional back propagation artificial neural network model, and had higher prediction accuracy…”

“…and convergence speed [4]. Different from their research, this study adopts random forest algorithm for early warning of gas explosion, and improves the model performance by optimizing parameters Mtry and Ntree.…”

“…In this study, the evaluation set of gas explosion disaster risk level in coal mine was established, P = {Safety; Safer; General safety; Less safe; Unsafe}, including 5 review levels. Each level corresponds to a corresponding security level vector (5,4,3,2,1). This set of methods and steps, combined with systematic analysis and professional advice, is helpful to accurately evaluate the risk of gas explosion disaster in coal mines, and turn it into an understandable safety level evaluation, which provides effective support and guidance for coal mine safety production.…”

Market Risk Early Warning Based on Deep Learning and Fruit Fly Optimization

“…SVM is a generalized linear classifier, which is proposed based on statistical learning theory and the principle of minimizing structural risk. Its basic principle is to construct an optimal hyperplane to maximize the distance between samples of two different categories, which is shown in Figure 1 [14]. Here, circles and squares represent two different types, respectively, and the optimal hyperplane is to maximize the range between the two dotted lines.…”

Research on a Risk Early Warning Mathematical Model Based on Data Mining in China’s Coal Mine Management

“…Risk prediction and early warning are widely used, mainly in finance [29], transportation [30][31][32], construction [33], process of industrial operations [34], human resource management [35], natural disasters [36,37], and metal dust and natural gas accidents [38,39]. In addition, the analytic hierarchy process, entropy weight method and multiple correlation number method have been used to analyze the static risk or dynamic risk on urban public safety, and the regional risk on urban public safety has been predicted [40].…”