Abstract:

Based on the displacement sequence of slope, the stability of slope could be judged effectively by forecasting the displacement of slope in the future. Through analyzing advantages and disadvantages of grey forecasting methods and least square support vector machines(LSSVM) respectively , a new forecasting model of grey least square support vector machine was proposed. The new model not only developed the advantages of accumulation generation of the grey forecasting method, weakened the effect of stochastic-disturbing factors in original sequence and strengthened the regularity of data, but also used the quickly solving speed and the excellent characteristics of least square support vector machines for nonlinear relationship and avoided the theoretical defects existing in the grey forecasting model. At the same time, the genetic algorithms were used to optimize the parameters of new model. At last, two engineering examples are given to testify the effectiveness of the grey least square support vector machine method to forecast displacements of slope; the results show that the new model has higher precision.

Key words: slope displacement, grey model, least square support vector machines, genetic algorithms, time sequence