ELMAN NEURAL NETWORK BASED TIME-SERIES FORECASTING MODEL FOR GROUND SURFACE MOVEMENT ON NO.2 NICKEL MINE AREA IN JINCHUAN

Artificial neural networks (ANNs) can be used for the ground surface movement prediction in the cases that traditional theories of subsidence and forecasting methods are not suitable, because they are based on the nonmetal mine underground mining. New methods are needed to deal with the ground surface movement problems in metal mine area such as Jinchuan Nickel mine with high dip angle. It is known that the Elman neural network can well approach any nonlinear continuous function and has ability to reflect dynamic features of the systems. Therefore, a time-series forecasting model of ground surface movement based on Elman neural network is presented. The datum of ground surface deformation got form GPS monitoring in Jinchuan Mine area were used to verify this model. Through comparing the forecasting result from the Elman model with the monitoring datum from GPS, it shows that the ANN prediction model is a useful method with good precision, especially under short time step prediction. The proposed method can offer a solution to the shortage of method in practice to a certain extent.