Coal Mine Safety Evaluation Based on Machine Learning: A BP Neural Network Model

Abstract

As the core of artificial intelligence, machine learning has strong application advantages in multi-criteria intelligent evaluation and decision-making. The level of sustainable development is of great significance to the safety evaluation of coal mining enterprises. BP neural network is a classical algorithm model in machine learning. In this paper, the BP neural network is applied to the sustainable development level decision-making and safety evaluation of coal mining enterprises. Based on the analysis of the evaluation method for sustainable development of coal enterprises, the evaluation index system of sustainable development of coal enterprises is established, and a multi-layer forward neural network model based on error backpropagation algorithm is constructed. Based on the system theory of man, machine, environment, and management, and taking the four single elements and the whole system in a coal mine as the research object, this paper systematically analyzes and studies the evaluation and continuous improvement of coal mine intrinsic safety. The BP neural network evaluation model is used to analyze and study the intrinsic safety of coal mines, the shortcomings of the intrinsic safety construction of coal mines are found, and then improvement measures are put forward to effectively promote the safe production of coal mines and finally realize the intrinsic safety goal of the coal mine.

Quality Evaluation of Ideological and Political Education in Universities Based on BP Neural Network

“…is study applies the relative error of data as the error signal to improve the standard BP algorithm (i.e., absolute error backpropagation algorithm) in order to improve the accuracy of the BP neural network. For establishing the neural network model with the function of "university teaching quality evaluation," this study adopts the "three-layer" BP network, the number of neurons in the first input layer (13 evaluation indexes affecting its teaching quality) is 13, the number of neurons in the second intermediate layer is 28, and the number of structural neurons in the third layer is 28. e number of neurons of the third output layer (teaching quality) is 1. e BP network [10][11][12][13][14] is trained by using the teaching quality evaluation data to find out the correlation between the teaching quality and the evaluation indexes of university teachers. en, they further rely on the trained BP network to calculate the teaching quality of teachers under different influencing factors (each evaluation index).…”

Risk Assessment of Green Intelligent Building Based on Artificial Intelligence

“…Neural network learning [13][14][15][16][17] is divided into supervised (with a teacher) learning and unsupervised (without a teacher) learning. In this paper, the neural network model is trained by a supervised learning method characterized by the training sample's expected output (one-to-one correspondence with the input) [18][19][20][21].…”

“…After selecting the best hyperparameters according to the descending curve of the loss function [17][18][19], the performance evaluation index R2_score, which measures the accuracy of the regression model, is used to test the model's accuracy. e maximum accuracy given by R2 is 100%, and the R2 score using the above evaluation model is R2_score � 0.91, which is ideal.…”

Using the ISM-ANP-SD combination model to explore the mechanism and intervention strategies of influencing factors of coal mine safety system

“…They classified the risks into 8 categories: Economic and financial, Environmental, Health and safety, Natural and external, Operational and technical, Organizational and managerial, Political and legal, and Socio-cultural ( 1 ). Based on the systematic theory of human, machine, environment, and management, Bai and Xu constructed the classification model of coal mine safety evaluation, constructed 14 evaluation index systems from four aspects of human, machine, management, and environment, and used BP neural network to evaluate coal mine safety ( 24 ). Ma established 30 evaluation index systems from five aspects: environmental disaster, safety management, facility performance, behavior monitoring, and emergency rescue.…”