Time series prediction of mining subsidence based on a SVM

Abstract

In order to study dynamic laws of surface movements over coal mines due to mining activities, a dynamic prediction model of surface movements was established, based on the theory of support vector machines (SVM) and times-series analysis. An engineering application was used to verify the correctness of the model. Measurements from observation stations were analyzed and processed to obtain equal-time interval surface movement data and subjected to tests of stationary, zero means and normality. Then the data were used to train the SVM model. A time series model was established to predict mining subsidence by rational choices of embedding dimensions and SVM parameters. MAPE and WIA were used as indicators to evaluate the accuracy of the model and for generalization performance. In the end, the model was used to predict future surface movements. Data from observation stations in Huaibei coal mining area were used as an example. The results show that the maximum absolute error of subsidence is 9 mm, the maximum relative error 1.5%, the maximum absolute error of displacement 7 mm and the maximum relative error 1.8%. The accuracy and reliability of the model meet the requirements of on-site engineering. The results of the study provide a new approach to investigate the dynamics of surface movements.