In order to use the characteristic information of different modeling methods sufficiently and improve the quality of prediction result

a nonlinear combining forecasting model for slope stability based on support vector machines with particle swarm optimization was presented. The new model could predict the safety factors using only characteristic parameters of slopes. Characteristic information of different modeling methods were integrated and the chanciness of single prediction method was avoided. In the interest of improving the forecasting accuracy

support vector machines which using a mixed kernel function was used; and the parameters of SVM were selected by PSO algorithm which had the characteristics of parallel and distributing. Applying the nonlinear combined forecasting model to predict safety factors of 10 slope cases after learning with other 73 samples; result showed that the model combining the characteristic information of different modeling methods very well. Compared with single predicting model

regressive combining model and BP network combining model

the PSO-SVM combining model had a higher predicting precision and smallest peak error; thus a new approach to estimate the slope stability more accurately was provided