Prediction and analysis of open pit slope deformation based on a GA-BP neural network

Due to various factors, mine slopes will be deformed. When the deformation exceeds a certain limit, it will cause great harm to the mining area. It can prevent disasters to a certain extent by carrying out slope deformation prediction and analysis. On the basis of fully considering that it is difficult to determine the initial weight and threshold of the BP neural network, which causes the model system to enter the local minimization and leads to the problem of low prediction accuracy, a GA-BP neural network prediction model that solves the deficiency of the ordinary network model in weight and threshold is proposed. Taking the slope deformation monitoring points JC31, JC33 and JC36 of the Yuebao open-pit mine as the research object, the gray theory model, BP neural network model and GA-BP model were used to predict the slope deformation. The results show that compared with the gray model and BP model, the predicted value of the GA-BP network model is more consistent with the actual value, the prediction accuracy is higher, and its average relative error is the smallest, which is more than 10 times higher than the other two methods, indicating that this method has certain reliability and feasibility.