Prediction of the Death Toll of Environmental Pollution in China’s Coal Mine Based on Metabolism-GM (1, n) Markov Model

The data of death toll in China’s coal mine cuased by environmental pollution is very difficult to predict because of its characteristics of small data volume, strong discreteness and non-linearity. In order to accurately predict the death toll in coal mine caused by environmental pollution, and take effective preventive measures to reduce casualties, in this paper, the grey GM (1, n) forecast model, Markov Theory and metabolic thought are combined. With the calculation of weight of grey Markov model by order relation analysis method, the predictive model is constructed, and a method which is suitable for predicting the death toll of environmental pollution in coal mineis proposed. Based on the death toll of coal mine traffic accidents from 2001 to 2013, the above model is used to predict the death toll of coal mine environmental pollution in 2014, and the result is compared with the actual data. The result shows that the relative error of the prediction on the death cuased by environmental pollution in China’s coal mine based on metabolism-GM (1, n) Markov model is 32.73% less than that of grey GM (1, n) prediction model. It can be seen that the proposed grey Markov model has high prediction accuracy and meets the actual demand. It is an effective method to predict the death of China’s coal mine generated by environmental pollution.