GM-SVM Prediction Model for Land Subsidence at Finished Underground Mining and its Application

摘要

Underground mining is one of the causes of land subsidence. The process of the land subsidence caused by underground raining is complicated and systematicness. Accurate prediction the land subsidence has important practical or immediate significance to avoid the harm of land subsidence. Grey system theory was applied extensively and had gained a series of achievements, but our preliminary study show that the general CM (1, 1) model was inadequate to handle land subsidence prediction as its only adapt to the data with exponential law. The advantages and disadvantages of grey forecasting methods and support vector machine (SVM) are analyzed respectively, this article proposes a new land subsidence settlement forecasting model of grey support vector machine. The new model develops the advantages of accumulation generation in the grey forecasting method, weakens the effect of stochastic disturbing factors in original sequence, strengthens the regularity of data. The example shows that the prediction accuracy has been improved quite a lot in comparison with general grey model.