Ground vibration is one of the most severe and complex environmental problems in blasting operations.Blasting vibration can have adverse effects on reserved slopes and structures,which would consequently affect the integrity of the building structures.Hence,an accurate prediction of ground vibrations during blasting process is of great significance.The main purpose of the research is to make highly precise predictions of the ground vibrations caused by mine blasting,and to reduce the impact of ground vibrations on the surrounding environment.In this study,an artificial neural network(ANN) model was put forward to predict blasting vibrations of Qianbaizaoshan Iron Mine.Besides,vibration data from 29 blasts were collected,and 8 ANN models were established for PPV prediction.In order to evaluate the established network,the coefficient of determination(R2),Root Mean Squared Error(RMSE) and Mean Square Error(MSE) were chosen as the network performance evaluation indexes.It is found that the 2-6-1 network has the best performance when R2 is 0.92,RMSE is 0.4 and MSE is 0.23.In order to prove the superiority of ANN forecasting method,four empirical models and multiple linear regression(MLR) models were used to predict PPV.The results show that the ANN model has better prediction performance than the empirical and MLR model.