Mine water inflow prediction based on GA-SVM

The accurate prediction of mine water inflow is very important to prevent mine water inrush accident. In this paper, the SVM model optimized by GA(GA-SVM) was put forward to realize the short term and accurate prediction of mine water inflow. In this method the automatic optimization function of GA was used to find the optimal parameters of SVM, which can improve the accuracy of prediction. Firstly, the optimal embedding dimension and delay time of mine water inflow were obtained by using the entropy ratio method, then the phase space was reconstructed. Secondly, the actual time series of water inflow from 2011-2015 in Qianqiu coal mine of Yima Coal Group Company were collected. GA-SVM model was used to predict the final 12 sets of data, the mean absolute percentage error was only 0.92%, the maxmum relative error was 2.62%. Finally, compared with the PSO-SVM and BP neural network method, the prediction results show that the proposed GA-SVM optimization model is suitable for the prediction of mine water inflow and the prediction accuracy is higher.