Forecast and analysis of coal mine safety accidents based on BP Neural Network and GM Model

With the rapid development of coal industry, China has become the largest coal-consuming country in the world, but coal mining accidents frequently happen. Therefore, safety situations in production cannot be ignored. Safety accident forecast is a crucial measure to reduce the accident incidence of coal mine enterprises. In this thesis, BP Neural Network and GM Model are combined to forecast and analyze safety accidents of coal mines. Improved forecast accuracy will provide coal mine enterprises with more precise data, on which they will base their scientific safety management.