Mine Subsided Prediction Model Based on SVM Combined With Fruit Fly Optimization Algorithm

As to the shortcomings of current mine subsided prediction methods, a new method was proposed. Fruit Fly Optimization Algorithm (FOA) was combined with Support Vector Machine (SVM), and FOA-SVM prediction model was built. Seam dip, mining thickness, average mining depth was selected as input parameters of the model; the maximum subsidence was output parameter. Selecting the training set sample, the optimal parameters of SVM was determined with applied FOA. The prediction model was tested by prediction set sample and the prediction performance was compared with other models. The results showed that the model has higher predictive ability and generalization ability compared with GA-SVM model, PSO-SVM model, BP neural network. The model could better realize mining subsidence prediction.