Prediction of the level of water inrush from coal seam floorbased on BP neural network

In order to reduce flood loss and improve the prediction accuracy of water inrush from coalseam floor,a prediction model of water inrush grade in Feicheng mining area was established based onthe mine hydrogeological data of this area,selecting six indexes including water pressure,developmentdegree of floor fissure,fault gap,karst development degree,aquifer thickness and aquiclude thickness.The method of mean impact value ( MIV) was used to evaluate the influence of each variable in theneural network model on the prediction of water inrush grade. The results show that this model has highprediction accuracy. Small water inrush in Feicheng mining area is mainly determined by the attributesof the aquifer and aquiclude. The occurrence of large and extra-large water inrush is closely related tothe factors such fault structure,karst and development degree of floor fissure.