Slope Stability Forecast of LS-SVM Based on Chaos Genetic Algorithm Optimization

As the relationship between geotechnical slope stability and influencing factors is complex and nonlinear, the least squares support vector machines (LS-SVM) is used to establish the nonlinear relation between slope stability and influencing factors. And in consideration of that parameters selection of LS-SVM exerts a major influence on modeling results, the parameters of LS-SVM are optimized by chaos genetic algorithm (CGA). Thus the CGA LSSVM is proposed for forecasting slope stability. Through the comparison between the method and the simple genetic algorithm (SGA) parameters optimization. The result shows that parameters optimization of the CGA has better faster convergence speed, higher prediction precision. And the model is applied to predict the safety factor of the actual slope engineering and the results are well consistent with the actual situation. It is shown that the model is reasonable and feasible.