Research on Miners’ Unsafe Behavior Based on Long and Short Term Memory

The emergence of unsafe behavior of miners is a complex nonlinear dynamic process. In order to predict the time series of unsafe behavior, the long and short term memory with a “memory” function and a solution to the disappearance of gradients is selected. Keras under TensorFlow was used to build a time series prediction model of unsafe behavior based on long and short term memory. A total of 3 405 time series data in coal mine A and B were used for model training and testing, and the optimal parameters were selected according to the cross validation set. The results showed that the minimum average absolute error of the four time series prediction models is 0.080 7 and the maximum average absolute error is 0.333 5 and those models can well predict unsafe behavior in a certain period of time in the coal mine.